A hands-on leader and Silicon Valley veteran, Stefan has spent over 15 years working across many parts of the stack. For the last decade, he's focused primarily on data and machine learning related systems and their connection to building product applications. He has built many 0 to 1 and 1 to 3 versions of these systems at places like Stanford, Honda Research, LinkedIn, Nextdoor, Idibon, and Stitch Fix.
A regular conference speaker, Stefan has guest lectured at Stanford’s Machine Learning Systems Design course & Building Apps with LLMs Inside course, and is an author of two popular open source frameworks called Hamilton & Burr.
", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/cF5D6tWKQauXWhU12UQz_stefan_right_cropped_reduced.jpg"}], "learning_outcomes": [{"uuid": "3fde9a82-faad-49d0-b184-688476cdaf18", "title": "Setup: the components that you need to have", "description": "Understand the different components required: document loading, parsing, text chunking, and embedding creation."}, {"uuid": "b2bc9526-372c-4753-871e-7b49ccd98042", "title": "Develop: we'll write some code", "description": "Set up a basic pipeline to parse some technical documentation to get you started using open source tools."}, {"uuid": "f82a4b39-a65e-4741-aed5-3d7df431836f", "title": "Caveats: going from development to production", "description": "Common caveats when taking such a system to production."}]}], "created_at": "2026-02-12T20:52:33.340542Z", "updated_at": null, "theme": null, "attrs": {"type": "workshop", "is_canceled": false, "is_delisted": false, "connected_course_id": null, "course_stripe_promo_code_id": null}, "school_event": {"id": 895, "slug": null, "title": "Build a Document Processing Pipeline for RAG Systems", "description": null, "start_datetime": "2024-05-06T18:00:00Z", "end_datetime": "2024-05-06T18:30:00Z", "start_date": "2024-05-06", "start_time": "11:00:00", "duration_min": 30, "timezone": "America/Los_Angeles", "has_internal_recording": true, "is_recording_public": true}, "num_signups": 609}, "relatedWorkshops": [{"id": 6220, "school_id": 13346, "is_visible_on_discovery_page": true, "is_canceled": false, "is_delisted": false, "connected_course_id": 14694, "workshop_tags": [{"id": 31, "tag_type": "category", "label": "Data & engineering", "slug": "engineering-ll", "description": null, "parent_tag_id": null}, {"id": 35, "tag_type": "category", "label": "AI", "slug": "ai-ll", "description": null, "parent_tag_id": null}, {"id": 137, "tag_type": "internal_collection", "label": "Instructor", "slug": "Instructor", "description": null, "parent_tag_id": null}, {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, {"id": 313, "tag_type": "topic", "label": "AI", "slug": "ai", "description": null, "parent_tag_id": null}, {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}], "published_content_page": {"id": 7003, "school_id": 13346, "slug": "15a343", "content_page_type": "workshop", "sections": [{"uuid": "d8b65f24-0e66-467c-adad-479e77e8e464", "title": "Build RAG System with LangChain", "version": 1, "image_url": "https://d2426xcxuh3ht5.cloudfront.net/XP7UEb4xQ6O1jKLqLySE_Build%20RAG.png", "is_visible": true, "topic_desc": "RAG is the most practical way for enterprises to extend LLM capabilities using proprietary data. LangChain provides the tools to build these systems efficiently, from document ingestion to secure retrieval and generation. This skill is now essential for building intelligent, scalable AI solutions for the enterprise.", "brand_logos": {"label": "AI Engineer & Tech Founder AiAgentCafe", "logos": [{"name": "NVIDIA", "type": "logo", "uuid": "2949eaf4-a489-4f46-9f9e-b87e01a57f00", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idLlih8n6_/idoyce87iK.svg"}, {"name": "Ogilvy", "type": "logo", "uuid": "baf788bb-721e-4acd-9472-c059570667c7", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idvSbgTL5k/idhGWM66cH.svg"}, {"name": "American Financial Group, Inc.", "type": "logo", "uuid": "652e4338-ca6d-4176-bcb7-2052e62bd7fe", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idhklqA91C/idvdIKFPFO.svg"}, {"name": "ICICI Bank", "type": "logo", "uuid": "6e50a199-9e5c-42f7-9f55-3ae865d77b61", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idJHpX8apR/id7HFP9fq3.svg"}, {"name": "National Bank of Kuwait", "type": "logo", "uuid": "a6568b3d-489a-48fe-b231-bc3acd29eb63", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idx2-zNRdy/idDwiPcrfr.svg"}]}, "section_type": "main", "instructor_infos": [{"name": "Nitin Monga", "uuid": "5e9b9aff-2d8b-4309-8597-74239f4b7fa1", "logos": null, "title": "Tech Founder AI Agent Cafe", "bio_html": "I believe managing is routine but building is an art. That’s where the real magic happens, & that is why I moved from a corporate life of managing projects & teams to entrepreneurship because I wanted to create, innovate, and see ideas take shape in the real world.
I’m the Founder of AI Agent Café, where we design & build intelligent AI agents, chatbots, & automation workflows that help startups, solopreneurs, and businesses scale smarter through AI.
When I’m not building or consulting, I teach and mentor professionals, empowering them to build the next generation of AI-powered products.
Portfolio - https://nitinai.xyz
LinkedIn - https://www.linkedin.com/in/nitinmonga-ai/
Advanced AI Course - https://maven.com/nitinai/agentic-ai-bootcamp
Career highlights
Jason has built search and recommendation systems for the past 6 years. He has consulted and advised a dozens startups in the last year to improve their RAG systems. He is the creator of the Instructor Python library.
", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/nFGwGYEqQ3aBtAesvn4z_Jason.png"}, {"name": "Adit Abraham", "uuid": "da5a85ce-7d76-4f2d-8897-c58dd3c88fe8", "title": "Co-founder & CEO of Reducto", "bio_html": "Adit is the co-founder and CEO of Reducto, a company that helps leading AI teams from startups through Fortune 10 parse and extract data from complex documents. Prior to Reducto, Adit studied CS at MIT and worked in ML research and product roles at MIT's Media Lab, BlinkAI, and Google.
", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/a9Hh5Rl6SXm5FG9AaSgk_Screenshot 2025-03-20 at 10.23.18 AM.png"}], "learning_outcomes": [{"uuid": "1fdf56ce-ad2b-4ff8-860b-a5787e0bcaf4", "title": "Parsing Precision", "description": "Learn precise document parsing techniques to extract high-quality data for accurate retrieval systems."}, {"uuid": "d0eeab65-a476-42bb-b147-029a1bd0172d", "title": "Optimal Data Representation", "description": "Apply specialized formatting strategies that maximize performance in both language and embedding models."}, {"uuid": "86715080-9f6a-4a18-a5ad-66fc9a1cde78", "title": "Enterprise-Scale RAG Solutions", "description": "Implement advanced ingestion methodologies that maintain quality while handling large-scale document collections."}]}], "created_at": "2026-02-12T21:00:19.720158Z", "updated_at": null, "theme": null, "attrs": {"type": "workshop", "is_canceled": false, "is_delisted": false, "connected_course_id": 12327, "course_stripe_promo_code_id": null}, "school_event": {"id": 3867, "slug": null, "title": "Better RAG Through Better Data", "description": null, "start_datetime": "2025-06-18T18:00:00Z", "end_datetime": "2025-06-18T19:00:00Z", "start_date": "2025-06-18", "start_time": "14:00:00", "duration_min": 60, "timezone": "America/Toronto", "has_internal_recording": true, "is_recording_public": true}, "num_signups": 798}}, {"id": 60, "school_id": 8651, "is_visible_on_discovery_page": true, "is_canceled": false, "is_delisted": false, "connected_course_id": null, "workshop_tags": [{"id": 35, "tag_type": "category", "label": "AI", "slug": "ai-ll", "description": null, "parent_tag_id": null}, {"id": 136, "tag_type": "internal_collection", "label": "LL Partner", "slug": "ll-partner", "description": null, "parent_tag_id": null}, {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, {"id": 299, "tag_type": "persona", "label": "For Data Scientists", "slug": "for-data-scientists", "description": null, "parent_tag_id": null}, {"id": 313, "tag_type": "topic", "label": "AI", "slug": "ai", "description": null, "parent_tag_id": null}, {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}], "published_content_page": {"id": 884, "school_id": 8651, "slug": "11439c", "content_page_type": "workshop", "sections": [{"uuid": "eed819ce-c331-4c69-bcc5-2d55b22f41a4", "title": "Semantic Search with Low-Code", "version": 1, "image_url": "https://d2426xcxuh3ht5.cloudfront.net/eZApsb27SkCX34ZMokzb_Lightninglesson_Social@2x (1).jpg", "is_visible": true, "topic_desc": "Semantic search is the foundation behind powerful search engines and chatbots. \n\nIn this session, you'll learn the quickest way to build and deploy semantic search solutions with minimal code. \n\nThis session is for anyone looking to enhance user experience, build smarter chatbots, and stay competitive in the rapidly evolving digital landscape.", "brand_logos": {"label": "Worked at", "logos": [{"name": "Tenstorrent", "type": "logo", "uuid": "ba6f2f98-65a7-47d2-ad5f-cb1602bb5a72", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idiuAwGjlv/idPVHGEMMH.svg"}, {"name": "Jina AI", "type": "logo", "uuid": "b1592e28-3761-49ce-a753-44a8b738da9e", "file_type": "png", "image_url": "https://asset.brandfetch.io/id7tTd5s7R/ideacX3Ie6.png"}, {"name": "Arcesium", "type": "logo", "uuid": "ff3a6e7d-78db-4dff-8de1-542a8f7943ff", "file_type": "svg", "image_url": "https://asset.brandfetch.io/id5cGpJKuj/idRzUgmwYk.svg"}]}, "section_type": "main", "instructor_infos": [{"name": "Shubham Saboo", "uuid": "7e1abcb6-a1fe-42ea-990b-85f07e3d0905", "title": "AI Author, Product Manager at Tenstorrent, Founder of Unwind AI", "bio_html": "Shubham is an Al product leader with hands-on experience in building Al products, communities, GTM strategy and transforming them into profitable businesses. Shubham has co-authored books on LLM and Neural Search. He is a Microsoft MVP and the Top Voice on LinkedIn. He also runs one of the top AI newsletters called Unwind AI to educate his audience about the latest AI developments and their impact.
", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/V46Dnp6GT92UUm127nWL_Q3oUhtHM_400x400.jpg"}], "learning_outcomes": [{"uuid": "53e13e7a-e616-4c0f-8634-57f3303aca36", "title": "Build RAG pipeline for semantic search", "description": "Create a powerful RAG pipeline for semantic search with a few lines of Python code"}, {"uuid": "138426d1-5dfc-4bba-ae26-9ccf0c73453c", "title": "Integrate data from multiple sources", "description": "Ingest data from multiple sources into a vector database with minimal Python code"}, {"uuid": "15efab2a-7076-4991-9108-db7bdadf4137", "title": "Deploy your application in a few clicks", "description": "Setup and deploy a semantic search pipeline end-to-end using Embedchain and Streamlit"}]}], "created_at": "2026-02-12T20:52:33.340542Z", "updated_at": null, "theme": null, "attrs": {"type": "workshop", "is_canceled": false, "is_delisted": false, "connected_course_id": null, "course_stripe_promo_code_id": null}, "school_event": {"id": 884, "slug": null, "title": "Semantic Search with Low-Code", "description": null, "start_datetime": "2024-05-03T18:00:00Z", "end_datetime": "2024-05-03T18:30:00Z", "start_date": "2024-05-03", "start_time": "13:00:00", "duration_min": 30, "timezone": "America/Chicago", "has_internal_recording": true, "is_recording_public": true}, "num_signups": 251}}], "exploreCourses": [{"objectID": "10144", "school_id": 3671, "school_name": "Hamza Farooq", "school_slug": "boring-bot", "course_id": 10144, "course_name": "Agent Engineering Bootcamp: Developers Edition", "course_slug": "advanced-llm", "course_description": "Master Advanced Techniques for Building and Optimizing Agentic RAG Systems and Multi-Agent Workflows — Designed for Builders", "clp_hero_media": {"media_type": "image_url", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/URmm4vvLS1GXofMY4boq_Copy of Lighting Lessons (2).jpg", "youtube_video_id": "dQw4w9WgXcQ", "vimeo_video_id": null}, "instructor_infos": [{"name": "Hamza Farooq", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/A49XTCbmRqGdgTdh59aw_profile_pic-removebg-enhanced.jpg", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/A49XTCbmRqGdgTdh59aw_profile_pic-removebg-enhanced.jpg", "uuid": "1bd3c4e1-5047-4ce1-a028-e5403366460c", "bio_html": "I am the founder of Traversaal.ai, an LLM-based startup dedicated to creating scalable, customizable, and cost-efficient language model solutions for enterprises.
With over 15 years of experience in machine learning, my journey has spanned three continents and seven countries, covering a diverse range of industries such as tech, telecommunications, finance, and retail.
As a former Senior Research Manager at Google and Walmart Labs, I have led data science and machine learning teams, focusing on optimization, natural language processing, recommender systems, and time series forecasting.
I am also an adjunct professor at UCLA, and Instructor for Stanford Continuing Studies where I bridge the gap between academic theory and real-world AI applications.
", "headline": "Founder | Ex-Google | Prof UCLA & UMN", "title": "Founder | Ex-Google |\nAdjunct UCLA & UMN, SCU | Venture Partner", "linkedin_url": "https://www.linkedin.com/in/hamzafarooq/", "twitter_handle": null, "custom_social_link": "https://substack.com/@boringbot", "logos": {"logos": [{"image_url": "https://asset.brandfetch.io/id6O2oGzv-/idjOb2YcqW.svg", "type": "logo", "file_type": "svg", "uuid": "71831ee4-c440-494f-b943-5160f636635c", "name": "Google"}, {"image_url": "https://asset.brandfetch.io/idoGsFQrHx/idfnjZQYMT.svg", "type": "logo", "file_type": "svg", "uuid": "feb0f776-03d1-4afa-8f74-c1faf464eb9a", "name": "Walmart"}, {"image_url": "https://asset.brandfetch.io/idjDRN9GJw/idKkDQYSmt.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "c1616b08-92ab-45c5-aa6d-7164a12c2430", "name": "University of Minnesota"}, {"image_url": "https://asset.brandfetch.io/id7iVZsi3j/idLGzrYc13.png", "type": "logo", "file_type": "png", "uuid": "8f81df14-18ca-4630-a365-c435cdf4a334", "name": "Stanford Continuing Studies"}, {"image_url": "https://asset.brandfetch.io/id8jxrwm3u/id-xuACPIm.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "12f4c1a1-3892-469b-9f7c-75cdbef07fb8", "name": "UCLA"}], "label": "Worked at:"}, "highlights": null, "highlights_html": "UCLA Anderson School of Management – Designed and taught Machine Learning System Design in the #1 globally ranked MSBA program (QS).
Author of \"Building an LLM Application from Scratch\" – A hands-on guide that’s become a practitioner’s bible for RAG and agentic systems.
Global Impact – Taught 10,000+ students worldwide through open education initiatives, with projects featured in Times Square twice.
Industry Expertise – Led AI projects with Google, and worldwide, including building secure Enterprise RAGs and regional language models
Bootstrapped Founder – CEO & Founder of Traversaal.ai, leading an 18-engineer global team building enterprise-grade AI agents
I build and teach AI, with a focus on practical, developer-first machine learning systems and modern AI infrastructure.
I am a Staff AI/ML Engineer - DX at Together AI, where I work at the intersection of large-scale models, tooling, and education to help engineers and teams effectively build with AI.
Previously, I worked on vector databases at Weaviate and earlier as a data scientist across applied industry roles.
I am an instructor with over 200,000 students across LinkedIn Learning, Coursera, and DeepLearning.AI, where I specialize in making advanced AI concepts accessible to practitioners. I’m deeply passionate about open source software, machine learning, and technical education, and to share that passion I’ve given talks at international conferences worldwide.
6 live sessions · 6 office hours · 1 demo day · 20+ hours
⚡ Agent Harness Design: Build a observable perceive–think–act loop in Python with structured tracing, evolving from simple LLM calls to full agent systems with tools, reflection, and planning
⚡ Agentic RAG Systems: Create advanced RAG pipelines with routing, multi-hop reasoning, Knowledge Graph, and semantic caching to improve accuracy and efficiency
⚡ Real-Time Voice Agents: Engineer a streaming STT → LLM → TTS pipeline with turn-taking, interruptions, and sub-second latency across modern voice platforms
⚡ Multi-Agent Orchestration: Design coordinated agent systems using MCP and A2A with patterns like hierarchical, debate, and orchestrator-worker workflows
⚡ Guardrails & Evaluation: Build reliable systems with safety guardrails and trajectory-based evaluation using LLM-as-judge and validated benchmark tasks
⚡ Production Deployment: Ship multi-agent systems with Google ADK, MCP, A2A, Llama Guard, and GCP monitoring
👉 This bootcamp is for engineers who ship real agent systems
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "23333c84-3ccd-493a-8658-0de652351133", "description_html": "Machine Learning Engineer exploring different techniques to scale LLM solutions
"}, {"uuid": "0f549ecc-2c77-4d4b-a459-c27d0920c519", "description_html": "Researcher, who would like to delve in to various aspects of open-source LLMs
"}, {"uuid": "131cf11c-e130-478b-9c53-5a76405f0a1a", "description_html": "Software Engineer, looking to learn how to integrate AI into their products
"}]}, "upmarket_clp_prerequisites": {"is_visible": false, "items": [{"uuid": "791eb04f-7e19-4cab-abc6-af85c0f85542", "name": "", "description": ""}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": "Master Advanced Techniques for Building and Optimizing Agentic RAG Systems and Multi-Agent Workflows — Designed for Builders", "items": [{"uuid": "1d755fcf-3dc5-4528-b0bc-8674b2956e78", "title": "Build and Debug Production Agent Harness", "items": ["Outcome: Engineer ReAct agents with structured tracing, decision auditing, memory, and termination control.", "Framework: Implement perceive-think-act loops, planning agents, reflection, and tool-use architectures.", "Hands-on: Building a production-grade agent harness and unpacking what makes Claude Code powerful beyond the model."]}, {"uuid": "5cc3d0c4-f565-4061-9955-45cf0f14267d", "title": "Deploy and Optimize LLM Inference", "items": ["Outcome: Serve production LLMs with predictable latency, lower cost, and benchmarks you can defend.", "Deep dive: Master GPTQ, GGUF, QLoRA, AWQ, prefill, decode, KV caching, and speculative decoding.", "Infrastructure: Measure TTFT, inter-token latency, throughput, and the economics of each deployment choice."]}, {"uuid": "e53b140b-a579-4d89-8f9b-fa0643b620a5", "title": "Ship Agentic RAG with Graphs and Semantic Cache", "items": ["Outcome: Build retrieval systems that route, reason, adapt, and move beyond naive top-k search.", "Framework: Implement retrieval-as-a-tool, query planning, multi-hop reasoning, and semantic chunking.", "Advanced: Add Graph RAG and semantic caching to improve reasoning quality, cost, and latency."]}, {"uuid": "c30110b1-87fe-48cd-8815-70e67e3479b6", "title": "Build Voice Agents That Feel Alive", "items": ["Outcome: Engineer real-time voice agents with sub-second responses and natural conversational flow.", "Architecture: Build streaming STT, LLM, and TTS loops with turn-taking, end detection, and barge-in.", "Tools: Compare Deepgram, ElevenLabs, OpenAI Realtime, Vapi, and Retell across latency, quality, and cost."]}, {"uuid": "f575fe88-c670-4fca-83c5-94b6134aa5e7", "title": "Operate Multi-Agent Systems with Real Coordination", "items": ["Outcome: Design multi-agent systems with clear topology, communication protocols, and operational judgment.", "Patterns: Implement orchestrator-worker, hierarchical, debate, and handoff workflows for real use cases.", "Protocols: Connect agents with MCP and A2A, and evaluate when single-agent systems are the better choice."]}, {"uuid": "7cbc1001-2c73-4edc-95a7-350664dc4bc5", "title": "Evaluate, Guardrail, and Operate with Confidence", "items": ["Outcome: Ship agent systems with measurable quality, layered safety, and production-ready eval loops.", "Guardrails: Implement Llama Guard, NeMo Guardrails, validation, and prompt-injection defenses.", "Evals: Build trajectory-based evals with golden tasks, partial-credit grading, and LLM-as-judge scoring."]}]}, "vector_string": null, "vector": null, "score": 1.803294}, {"objectID": "15495", "school_id": 14003, "school_name": "Cheat at Search", "school_slug": "softwaredoug", "course_id": 15495, "course_name": "Cheat at Search with Agents", "course_slug": "cheat-at-search", "course_description": "Rethink search to put tireless agents at work for human users", "clp_hero_media": {"media_type": "image_url", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/8TD7KI2RQWhiyPfPHyeB_cheat-at-search-brain2.png", "youtube_video_id": "dQw4w9WgXcQ", "vimeo_video_id": null}, "instructor_infos": [{"name": "Doug Turnbull", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/rDTq3o4ZSc6qb0PmwzFv_dougie-in-snuggie-8-bit.png", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/rDTq3o4ZSc6qb0PmwzFv_dougie-in-snuggie-8-bit.png", "uuid": "a012f0df-ce5b-4b90-abfe-c7b7d60f3da8", "bio_html": "In 2012, Doug got bit by the search bug and he's still trying to keep up. From full-text search, to Learning to Rank models, to search agents that generate their own code, he knows the endless landscape first hand. Yet Doug wants to deeply understand the what / how / why, and help teams use these technologies practically, distinguishing hype from reality.
", "headline": "http://softwaredoug.com", "title": "Snuggie and Search Enthusiast", "linkedin_url": null, "twitter_handle": null, "custom_social_link": null, "logos": {"logos": [], "label": null}, "highlights": null, "highlights_html": "He’s led search at Reddit, Shopify, and Wikipedia, authored Relevant Search and AI Powered Search, and advised 100+ organizations over the years - all in pursuit of the same question: how does search actually work?
Keyword search + an agent - is that all we need now?
The agentic world challenges traditional ways of building search. Traditionally we've built thick, complex search APIs - query understanding, rerankers, learning to rank, vector databases. Yet an agent can compete in deep research benchmarks with the dumbest of search tools.
This course pushes you to rethink search. We teach:
Replacing search with agents - A search agent learns about retrieval systems, tailoring searches, achieving competitive results
Guiding agents, not algorithms - Search developers won't train algorithms. They'll build harnesses
Steering with an LLM judge - Integrate human and clickstream feedback to guide the agent's judgment towards what users want
Autoresearching to smarter search - Use + build a coding agent that optimizes retrieval code like an ML model.
Come learn alongside search industry expert Doug Turnbull as we unpack how agents will rewrite the search industry.
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "61fe4ac0-6f1a-47d2-ae9f-b30ad07159ec", "description_html": "Search technologists that want to learn how to ensure RAG and agentic search win
"}, {"uuid": "239b3729-6f70-4bf1-a54b-6166b5b6468d", "description_html": "Startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices
"}, {"uuid": "f3f1e283-92e2-47f0-8e6f-9399278393f7", "description_html": "Data scientists that want to consider how agentic / LLM / RAG based search can be oriented towards user feedback and business outcomes
"}]}, "upmarket_clp_prerequisites": {"is_visible": true, "items": [{"uuid": "2d080d7f-2bab-41a3-a1d3-4cb93ef55066", "name": "Lexical Search basics (do you know what BM25 is)", "description": "We will build on top of existing, core search knowledge"}, {"uuid": "efa08f09-c791-4ee4-a2fc-d9ae53ebf749", "name": "Basic search evaluation (judgments + metrics)", "description": "We will use common search datasets that use metrics like NDCG, etc to measure search"}, {"uuid": "f762411b-e2ce-4352-9fee-ee55b4d54f51", "name": "Vector search basics (do you know what an embedding is?)", "description": "Vector search will be one tool in our toolkit we'll apply to the agentic tools that we build"}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": "Rethink search to put tireless agents at work for human users", "items": [{"uuid": "79cd1921-491e-4640-a850-339928dc8d54", "title": "What makes a great search tool to an agent?", "items": ["Build search backends agents can reason about", "Why agents love simplicity over complexity", "Focusing tools on domain tasks, not \"search\""]}, {"uuid": "5a6450da-9dc6-4eff-bfee-91e93ba3bb1c", "title": "Use LLMs to help agents structure, organize, and find content", "items": ["Sidestep complex NLP to better understand queries", "Iterate on query understanding through agentic approaches", "Organize and structure content to be retrievable by an agent"]}, {"uuid": "9be3fa65-b762-4c02-be6a-cbfaa4abc0c4", "title": "Agentic results without the cost", "items": ["How to integrate agentic approaches into a traditional search stack", "Best practices to manage the agentic loop to save tokens, money, and prevent context rot", "Technical approaches to extracting agentic insights (ie code generation)"]}, {"uuid": "d9250325-eec8-4bc6-834d-35261dce9e06", "title": "Orienting agentic search towards the human, not the machi", "items": ["An agent will by default use its judgments to iterate on relevance on its own. But shouldn't it use humans?", "Integrating external feedback (user clicks, etc) into agentic relevance", "Beyond LLM as a Judge to exploring how to iterate and improve retrieval for agents and humans"]}]}, "vector_string": null, "vector": null, "score": 1.786768}, {"objectID": "17433", "school_id": 15363, "school_name": "AI-Powered Search", "school_slug": "search-school", "course_id": 17433, "course_name": "AI-Powered Search: Modern Retrieval for Humans & Agents", "course_slug": "ai-powered-search", "course_description": "Learn the latest AI Search skills with the authors of \"AI-Powered Search\"", "clp_hero_media": {"media_type": "image_url", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/6CuZOz7iRwC6dJ03szwS_6.jpg", "youtube_video_id": "dQw4w9WgXcQ", "vimeo_video_id": null}, "instructor_infos": [{"name": "Trey Grainger", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/b9WUgJuUS0CTF1uKoCMI_trey-maven.png", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/b9WUgJuUS0CTF1uKoCMI_trey-maven.png", "uuid": "4ccdaf7f-3a29-40ad-ac23-69f0415a154b", "bio_html": "Trey Grainger is lead author of the book AI-Powered Search (Manning 2025) and founder of Searchkernel, a software consultancy building the next generation of AI-powered search. He also serves as a technical advisor at OpenSource Connections. He previously served as CTO of Presearch, a decentralized web search engine, and as Chief Algorithms Officer and SVP of Engineering at Lucidworks, a search company whose technology powers hundreds of the world’s leading organizations. Trey is also co-author of the book Solr in Action (Manning 2014), as well as over a dozen other publications including books, journals, and research papers. Trey has 18 years of experience in search and data science focused on building self-learning search platforms integrating the most successful AI Search techniques.
", "headline": "AI Search Expert | Founder | Author", "title": "Founder, Searchkernel. Author, \"AI-Powered Search\". 18+ years leading search.", "linkedin_url": "", "twitter_handle": "", "custom_social_link": "", "logos": {"logos": [{"image_url": "https://asset.brandfetch.io/idskYMg_Lc/idsgrCljLm.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "b75ee738-3f6b-4bfc-9aa1-0f462b98b234", "name": "Lucidworks"}, {"image_url": "https://asset.brandfetch.io/idTCWdN3V2/idDlXTsZYw.svg", "type": "logo", "file_type": "svg", "uuid": "22f6351e-326f-43b6-8e4f-df7d6dbaa781", "name": "Presearch"}, {"image_url": "https://asset.brandfetch.io/id_QgB787m/idRRMNVm56.png", "type": "logo", "file_type": "png", "uuid": "01134be2-30de-434c-950c-5e6307cc3044", "name": "Optimized AI Conference"}, {"image_url": "https://asset.brandfetch.io/idRrcMU04X/idPe8dAyv6.svg", "type": "logo", "file_type": "svg", "uuid": "f09015eb-7439-485a-83c9-ef32ca843ed4", "name": "CareerBuilder"}, {"image_url": "https://asset.brandfetch.io/idJTONuDZB/idvnLUMzvj.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "0d22dbbe-8b23-4c84-9db7-9c9679b4dc05", "name": "Searchkernel"}], "label": ""}, "highlights": null, "highlights_html": "Since 2010, Doug Turnbull has worked with companies such as Shopify, Careerbuilder, Wikimedia Foundation, Shipt, and LexisNexis on improving search experiences. Doug coaches and develops search teams from startups to Fortune 500 companies, setting up organizational practices to build relevant search. Doug wrote “Relevant Search” (2016) and co-authored “AI Powered Search” (2022). He also co-created the Elasticsearch Learning to Rank, bringing machine learning to the most popular search engine, which revamped Wikipedia and Yelp, driving intelligent search behind dozens of companies' search experiences.
", "headline": "Principal Search Engineer | Author", "title": "Principal Search Engineer, Author of \"AI-Powered Search\" & \"Relevant Search\".", "linkedin_url": "", "twitter_handle": "", "custom_social_link": "", "logos": {"logos": [{"image_url": "https://asset.brandfetch.io/idAgPm7IvG/id85L14isQ.svg", "type": "logo", "file_type": "svg", "uuid": "c9cf80b5-b0bc-426e-b0eb-d91807bb1e55", "name": "Shopify.com"}, {"image_url": "https://asset.brandfetch.io/id2VbA0wwD/idv8cqA42F.png", "type": "icon", "file_type": "png", "uuid": "d635656d-e32e-4a9f-b69e-e7edd208531b", "name": "OpenSource Connections"}, {"image_url": "https://asset.brandfetch.io/idkKwm0IT0/idklqyFZ_8.svg", "type": "logo", "file_type": "svg", "uuid": "705bfccc-13c4-47cf-ae4d-b681871bd8c9", "name": "Reddit"}, {"image_url": "https://asset.brandfetch.io/idAo3WRIoq/idpY-V37uF.svg", "type": "logo", "file_type": "svg", "uuid": "f5985ff7-9cc4-4b06-b676-00562d7208c1", "name": "Wikipedia"}, {"image_url": "https://asset.brandfetch.io/idvwtgSsRX/idESHQtUtB.svg", "type": "logo", "file_type": "svg", "uuid": "f2db2ed5-cce4-4751-bf87-e95fc887c870", "name": "Yelp"}], "label": "\n"}, "highlights": null, "highlights_html": "AI Search is among of the most in-demand AI Skills. AI systems need RAG, and implementing great retrieval can often have a bigger impact than prompt engineering for improving your AI and Agentic systems.
Most search systems barely scratch the surface of what's possible. While others struggle with irrelevant results and frustrated users, you'll master the cutting-edge techniques that power the world's most successful search experiences—from Reddit and Spotify to the Fortune 500.
Why This Course Exists
The Problem: Many traditional search systems are stuck in the 2010s. But modern AI courses hyper focus on text embeddings and ignore many of the fundamentals of information retrieval. The result? Engineers who can't bridge the gap between amazing demos and production systems that actually work.
Our Solution: Stop downloading random embeddings from Hugging Face or OpenAI and hoping for the best. Learn to systematically analyze your users' actual intent, integrate domain context, and build search that truly understands what people (and agents) need—not just what they type.
This course helps you build cutting-edge AI Search systems that actually work.
Ready to level-up your AI Search skills?
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "b7476dc1-88ae-4854-a239-1a2de5d2232d", "description_html": "AI Engineers optimizing search ranking to measurably improve user search satisfaction
"}, {"uuid": "e4ffbbb2-fbe6-4cf9-a902-87968c588f18", "description_html": "Search Practitioners that want to learn to systematically improve search relevance
"}, {"uuid": "7a159c00-ca40-4b27-99dd-90bfc26f2c42", "description_html": "Technology leaders hoping to plan search experiments to maximize clicks and conversions
"}]}, "upmarket_clp_prerequisites": {"is_visible": true, "items": [{"uuid": "21de8992-2ef8-4e3f-bac8-24efcfd14517", "name": "Basic Python Programming", "description": "Examples will be taught in Python, it's a good idea to have basic Python familiarity under your belt"}, {"uuid": "1cf5c579-e07d-41df-aa33-6718ad62fb85", "name": "(Included Complimentary with the Course): Search Fundamentals", "description": "For those new to search/retrieval, we provide an optional intro session covering core matching & ranking concepts (BM25, vector spaces, etc)"}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": "Learn the latest AI Search skills with the authors of \"AI-Powered Search\"", "items": [{"uuid": "9545e36d-c629-4eaa-9850-e6405ef57853", "title": "Optimize your Agentic Search and Retrieval Augmented Generation (RAG) systems", "items": ["Understand emerging Agentic Search strategies, implement them with the Agentic Search loop, and be at the forefront of this paradigm shift.", "Optimize search for LLMs and agents, as well as the critical strategies (chunking, adaptive queries, guardrails, etc.)", "RAG represents the largest growing use case for search, and \"retrieval\" is by far the hardest part of RAG. "]}, {"uuid": "8dede459-118b-4f26-9c6d-0d061f46eb99", "title": "Go beyond the query to understand intent", "items": ["Learn how to integrate your content's context, your domain context, and your users' individual contexts to optimally interpret query intent.", "We'll cover query classification, query-sense disambiguation, and semantic query parsing, using multiple lexical and LLM-based strategies.", "Dig into emerging techniques like Wormhole Vectors, sparse lexical / expansion models (Semantic Knowledge Graphs and SPLADE), and MUVERA."]}, {"uuid": "cfde0238-91e7-47f9-9fe1-b4efccb4e0aa", "title": "Fill in holes in your modern information retrieval knowledge", "items": ["The field of information retrieval is vast. Learn the mental models that tie it all together. ", "Understand when to use bi-encoders vs. cross-encoders vs. knowledge graphs, and ANN vs. quantization vs. representation learning.", "Understand multimodal and hybrid search, search on semi-structured embeddings, and how to properly mix sparse + dense retrieval techniques."]}, {"uuid": "e78585b0-3729-4942-a29e-2b90eb009524", "title": "Improve relevance with crowdsourced ranking models built from click-stream data", "items": ["Leverage \"reflected intelligence\" from your use behavioral signals to automatically improve your ranking models.", "Learn to implement algorithms for signals boosting, matrix factorization, and knowledge graph learning", "Implement personalized search by building real-time user behavioral embeddings leveraging matrix factorization / collaborative filtering."]}, {"uuid": "549c0f6c-4579-4cf8-a7bd-7fc5b97bb79a", "title": "Optimize AI-Powered Search Models for Production", "items": ["Learn common Machine-Learned Ranking (Learning to Rank) models put into production, from LambdaMART to cross-encoders", "Get hands on experience performing feature engineering and representation learning to optimizing AI Search for production.", "Implement ANN and quantization strategies, reranking, semantic caching, and even local model serving for search and RAG."]}, {"uuid": "cd8c166b-eb72-47e5-bf42-043f56414e2b", "title": "Build ranking features & automate judgements with click models & Active Learning", "items": ["Learn how to systematically develop great ranking features using a combination of vector and lexical search indices.", "Generate automatic judgments from user click stream data using Click Models", "Overcoming ranking biases (presentation, position, & confidence bias) + uncover unexplored ranking features using SDBNs and Active Learning."]}]}, "vector_string": null, "vector": null, "score": 1.8090607}, {"objectID": "19715", "school_id": 15920, "school_name": "Alina Bezchotnikova ", "school_slug": "alina-bezchotnikova-k", "course_id": 19715, "course_name": "Automate Your Research Pipeline Without Loosing Quality", "course_slug": "automate-your-research-pipeline-without-loosing-quality", "course_description": "It's not enough to \"just add AI\"", "clp_hero_media": null, "instructor_infos": [{"name": "Alina Bezchotnikova ", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/1JL47ccMTGecTmRJVsj2_AK%201.png", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/1JL47ccMTGecTmRJVsj2_AK%201.png", "uuid": "a080f9be-33ff-474b-9924-1bcd11fa18d4", "bio_html": "The biggest waste in B2B SaaS - measuring the wrong things and chasing AI hype instead of evidence. I've spent the past decade at the intersection of UX, product metrics, and behavioral science, figuring out what actually moves business outcomes in complex enterprise products. I hold a PhD focused on gamification and user engagement, and a Master's in Statistics. I don't guess what works - I measure it. Currently I'm a Principal UX Designer at Aize, where I focus on product analytics and bridging the gap between user research and real product decisions. What I'm focused on right now: product metrics that predict business outcomes, AI-first user research, gamification in B2B SaaS. If you care about UX and Research rigor, AI-native research, or product metrics that matter - follow along.
", "headline": "UX & Research Strategy Advisor", "title": "Principal UX Designer, 17 years B2B SaaS, Master’s in Statistics, PhD", "linkedin_url": "https://www.linkedin.com/in/alina-bezchotnikova-000288236/", "twitter_handle": null, "custom_social_link": "https://uxrdi.com/", "logos": {"logos": [], "label": null}, "highlights": null, "highlights_html": "Principal UX Designer and Researcher with 17 years of experience working in B2B SaaS
Red Dot 2020
MS in Statistics
PhD in Communications with a specialisation in Gamification
Most product teams are stuck in one of two traps:
Trap #1: Research is too slow: By the time insights reach the team, the decision has already been made. Research becomes a post-hoc justification instead of a strategic input.
Trap #2: “Just use AI”: Teams replace research with LLM-generated guesses and call it “moving fast.” They get speed but lose the signal, the actual evidence of what users do, think, and need.
There’s a third path.
Automate the repetitive, time-consuming parts of your research pipeline as recruitment screening, transcript processing, pattern tagging, synthesis formatting, while keeping human judgment exactly where it adds irreplaceable value: interpretation, context, and strategic framing.
This workshop teaches you how to build that pipeline, step by step.
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "fe1d8c10-c2ac-43f8-822e-d37c0398cca9", "description_html": "Product Managers who run (or want to run) research but don’t have a dedicated researcher on the team
"}, {"uuid": "223de380-cfe2-47e2-95c2-7d22f77a0057", "description_html": "Product Leaders (Directors, VPs, CPOs) building a research culture that scales without scaling headcount
"}, {"uuid": "32ccaa03-f7f6-4661-986a-3be8825bf5d5", "description_html": "Founders who need evidence-based decisions but can’t afford slow, traditional research cycles
"}]}, "upmarket_clp_prerequisites": {"is_visible": false, "items": [{"uuid": "f38e4ba1-f495-4dcb-ab32-9e81bf539665", "name": "", "description": ""}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": null, "items": [{"uuid": "1954b1cc-d359-4c48-a97e-c73bd27c4709", "title": "", "items": ["", "", ""]}]}, "vector_string": null, "vector": null, "score": 1.7857354}], "workshopTags": [{"id": 136, "tag_type": "internal_collection", "label": "LL Partner", "slug": "ll-partner", "description": null, "parent_tag_id": null}, {"id": 32, "tag_type": "deprecated", "label": "Data", "slug": "x-deprecated-data", "description": null, "parent_tag_id": null}, {"id": 31, "tag_type": "category", "label": "Data & engineering", "slug": "engineering-ll", "description": null, "parent_tag_id": null}, {"id": 35, "tag_type": "category", "label": "AI", "slug": "ai-ll", "description": null, "parent_tag_id": null}, {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}, {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, {"id": 299, "tag_type": "persona", "label": "For Data Scientists", "slug": "for-data-scientists", "description": null, "parent_tag_id": null}, {"id": 313, "tag_type": "topic", "label": "AI", "slug": "ai", "description": null, "parent_tag_id": null}], "videoChapters": [{"title": "Introduction and Session Overview", "start_seconds": 3}, {"title": "Agenda and Key Takeaways", "start_seconds": 52}, {"title": "What is Retrieval-Augmented Generation (RAG)?", "start_seconds": 143}, {"title": "The 5 Steps of a Document Processing Pipeline", "start_seconds": 248}, {"title": "Step-by-Step Example: Loading, Extracting, and Chunking", "start_seconds": 311}, {"title": "Step-by-Step Example: Embedding and Storing", "start_seconds": 430}, {"title": "Live Code Demo Introduction", "start_seconds": 516}, {"title": "Q&A: RAG with Knowledge Graphs and Heterogeneous Data", "start_seconds": 560}, {"title": "Code Demo: Setup and Data Structures", "start_seconds": 743}, {"title": "Code Demo: Loading, Extracting, and Chunking", "start_seconds": 833}, {"title": "Code Demo: Embedding, Storing, and Execution", "start_seconds": 975}, {"title": "Production Caveats: Domain Specificity and Execution at Scale", "start_seconds": 1067}, {"title": "Final Remarks and Q&A", "start_seconds": 1170}, {"title": "Q&A: Hamilton's Capabilities and Chunking Strategies", "start_seconds": 1294}, {"title": "Q&A: Hamilton Tutorials, Cloud Environments, and Burr", "start_seconds": 1416}], "navPages": [{"category": {"slug": "ai", "href": "/courses/ai", "title": "AI", "palette": {"--palette-category-base": "#460952", "--palette-category-highlight": "#931FAA", "--palette-category-border": "#EDA6FB"}}, "topics": [{"title": "Agentic AI", "slug": "ai/agentic-ai", "href": "/courses/ai/agentic-ai"}, {"title": "Coding with AI", "slug": "ai/ai-coding", "href": "/courses/ai/ai-coding"}, {"title": "AI Workflows", "slug": "ai/ai-workflows", "href": "/courses/ai/ai-workflows"}, {"title": "Claude Code", "slug": "ai/claude-code", "href": "/courses/ai/claude-code"}, {"title": "OpenClaw", "slug": "ai/openclaw", "href": "/courses/ai/openclaw"}, {"title": "Vibe Coding", "slug": "ai/vibe-coding", "href": "/courses/ai/vibe-coding"}, {"title": "AI Evals", "slug": "ai/evals", "href": "/courses/ai/evals"}, {"title": "AI Transformation", "slug": "ai/ai-transformation", "href": "/courses/ai/ai-transformation"}, {"title": "RAG & Search", "slug": "ai/rag-search", "href": "/courses/ai/rag-search"}, {"title": "MCP", "slug": "ai/mcp", "href": "/courses/ai/mcp"}, {"title": "AI for PMs", "slug": "ai/for-product-managers", "href": "/courses/ai/for-product-managers"}, {"title": "AI for Engineers", "slug": "ai/for-engineers", "href": "/courses/ai/for-engineers"}, {"title": "AI for Designers", "slug": "ai/for-designers", "href": "/courses/ai/for-designers"}, {"title": "AI for Marketers", "slug": "ai/for-marketers", "href": "/courses/ai/for-marketers"}, {"title": "AI for Founders", "slug": "ai/for-founders", "href": "/courses/ai/for-founders"}]}, {"category": {"slug": "product", "href": "/courses/product", "title": "Product", "palette": {"--palette-category-base": "#510B00", "--palette-category-highlight": "#B33600", "--palette-category-border": "#F7804D"}}, "topics": [{"title": "AI for PMs", "slug": "for-product-managers/ai", "href": "/courses/for-product-managers/ai"}, {"title": "Agentic AI", "slug": "for-product-managers/agentic-ai", "href": "/courses/for-product-managers/agentic-ai"}, {"title": "AI Evals", "slug": "for-product-managers/ai-evals", "href": "/courses/for-product-managers/ai-evals"}, {"title": "Vibe Coding", "slug": "for-product-managers/vibe-coding", "href": "/courses/for-product-managers/vibe-coding"}, {"title": "Product Sense", "slug": "for-product-managers/product-sense", "href": "/courses/for-product-managers/product-sense"}, {"title": "Product Discovery", "slug": "for-product-managers/product-discovery", "href": "/courses/for-product-managers/product-discovery"}, {"title": "User Research", "slug": "for-product-managers/user-research", "href": "/courses/for-product-managers/user-research"}, {"title": "Prototyping", "slug": "for-product-managers/prototyping", "href": "/courses/for-product-managers/prototyping"}, {"title": "Growth", "slug": "for-product-managers/growth", "href": "/courses/for-product-managers/growth"}, {"title": "Analytics", "slug": "for-product-managers/analytics", "href": "/courses/for-product-managers/analytics"}, {"title": "Tech Foundations", "slug": "for-product-managers/technical-foundations", "href": "/courses/for-product-managers/technical-foundations"}, {"title": "Strategy", "slug": "for-product-managers/strategy", "href": "/courses/for-product-managers/strategy"}, {"title": "Influence", "slug": "for-product-managers/influence", "href": "/courses/for-product-managers/influence"}, {"title": "Leadership", "slug": "for-product-managers/leadership", "href": "/courses/for-product-managers/leadership"}, {"title": "Career Growth", "slug": "for-product-managers/career-growth", "href": "/courses/for-product-managers/career-growth"}]}, {"category": {"slug": "engineering", "href": "/courses/engineering", "title": "Engineering", "palette": {"--palette-category-base": "#00332F", "--palette-category-highlight": "#00746B", "--palette-category-border": "#88B7B2"}}, "topics": [{"title": "AI for Engineers", "slug": "for-engineers/ai", "href": "/courses/for-engineers/ai"}, {"title": "Agentic AI", "slug": "for-engineers/agentic-ai", "href": "/courses/for-engineers/agentic-ai"}, {"title": "Coding with AI", "slug": "for-engineers/ai-coding", "href": "/courses/for-engineers/ai-coding"}, {"title": "Claude Code", "slug": "for-engineers/claude-code", "href": "/courses/for-engineers/claude-code"}, {"title": "OpenClaw", "slug": "for-engineers/openclaw", "href": "/courses/for-engineers/openclaw"}, {"title": "MCP", "slug": "for-engineers/mcp", "href": "/courses/for-engineers/mcp"}, {"title": "RAG & Search", "slug": "for-engineers/rag-search", "href": "/courses/for-engineers/rag-search"}, {"title": "AI Evals", "slug": "for-engineers/ai-evals", "href": "/courses/for-engineers/ai-evals"}, {"title": "Machine Learning", "slug": "for-engineers/ml", "href": "/courses/for-engineers/ml"}, {"title": "LLM Ops", "slug": "for-engineers/llm-ops", "href": "/courses/for-engineers/llm-ops"}, {"title": "Context Eng", "slug": "for-engineers/context-engineering", "href": "/courses/for-engineers/context-engineering"}, {"title": "Security", "slug": "for-engineers/security", "href": "/courses/for-engineers/security"}, {"title": "System Design", "slug": "for-engineers/system-design", "href": "/courses/for-engineers/system-design"}, {"title": "Leadership", "slug": "for-engineers/leadership", "href": "/courses/for-engineers/leadership"}, {"title": "Career Growth", "slug": "for-engineers/career-growth", "href": "/courses/for-engineers/career-growth"}]}, {"category": {"slug": "design", "href": "/courses/design", "title": "Design", "palette": {"--palette-category-base": "#38085F", "--palette-category-highlight": "#772CBB", "--palette-category-border": "#B5A2D0"}}, "topics": [{"title": "AI for Designers", "slug": "for-designers/ai", "href": "/courses/for-designers/ai"}, {"title": "Agentic AI", "slug": "for-designers/agentic-ai", "href": "/courses/for-designers/agentic-ai"}, {"title": "Vibe Coding", "slug": "for-designers/vibe-coding", "href": "/courses/for-designers/vibe-coding"}, {"title": "Prototyping", "slug": "for-designers/prototyping", "href": "/courses/for-designers/prototyping"}, {"title": "Figma", "slug": "for-designers/figma", "href": "/courses/for-designers/figma"}, {"title": "Design Systems", "slug": "for-designers/design-systems", "href": "/courses/for-designers/design-systems"}, {"title": "User Research", "slug": "for-designers/user-research", "href": "/courses/for-designers/user-research"}, {"title": "Product Discovery", "slug": "for-designers/product-discovery", "href": "/courses/for-designers/product-discovery"}, {"title": "UX", "slug": "for-designers/ux", "href": "/courses/for-designers/ux"}, {"title": "UI", "slug": "for-designers/ui", "href": "/courses/for-designers/ui"}, {"title": "Visual Design", "slug": "for-designers/visual-design", "href": "/courses/for-designers/visual-design"}, {"title": "Design Strategy", "slug": "for-designers/strategy", "href": "/courses/for-designers/strategy"}, {"title": "Influence", "slug": "for-designers/influence", "href": "/courses/for-designers/influence"}, {"title": "Leadership", "slug": "for-designers/leadership", "href": "/courses/for-designers/leadership"}, {"title": "Career Growth", "slug": "for-designers/career-growth", "href": "/courses/for-designers/career-growth"}]}, {"category": {"slug": "marketing", "href": "/courses/marketing", "title": "Marketing", "palette": {"--palette-category-base": "#431532", "--palette-category-highlight": "#943170", "--palette-category-border": "#C99EB5"}}, "topics": [{"title": "AI for Marketers", "slug": "for-marketers/ai", "href": "/courses/for-marketers/ai"}, {"title": "Agentic AI", "slug": "for-marketers/agentic-ai", "href": "/courses/for-marketers/agentic-ai"}, {"title": "Vibe Coding", "slug": "for-marketers/vibe-coding", "href": "/courses/for-marketers/vibe-coding"}, {"title": "Automation", "slug": "for-marketers/marketing-automation", "href": "/courses/for-marketers/marketing-automation"}, {"title": "Content Marketing", "slug": "for-marketers/content-marketing", "href": "/courses/for-marketers/content-marketing"}, {"title": "Demand Gen", "slug": "for-marketers/demand-generation", "href": "/courses/for-marketers/demand-generation"}, {"title": "Go-to-Market", "slug": "for-marketers/gtm", "href": "/courses/for-marketers/gtm"}, {"title": "Product Marketing", "slug": "for-marketers/product-marketing", "href": "/courses/for-marketers/product-marketing"}, {"title": "Positioning", "slug": "for-marketers/positioning", "href": "/courses/for-marketers/positioning"}, {"title": "Social Media", "slug": "for-marketers/social-media", "href": "/courses/for-marketers/social-media"}, {"title": "Brand", "slug": "for-marketers/brand", "href": "/courses/for-marketers/brand"}, {"title": "B2B Marketing", "slug": "for-marketers/b2b", "href": "/courses/for-marketers/b2b"}, {"title": "SEO & AEO", "slug": "for-marketers/seo-aeo", "href": "/courses/for-marketers/seo-aeo"}, {"title": "Strategy", "slug": "for-marketers/strategy", "href": "/courses/for-marketers/strategy"}, {"title": "Leadership", "slug": "for-marketers/leadership", "href": "/courses/for-marketers/leadership"}]}, {"category": {"slug": "leadership", "href": "/courses/leadership", "title": "Leadership", "palette": {"--palette-category-base": "#50090E", "--palette-category-highlight": "#A9202A", "--palette-category-border": "#D49C98"}}, "topics": [{"title": "AI for Leaders", "slug": "for-leaders/ai", "href": "/courses/for-leaders/ai"}, {"title": "Agentic AI", "slug": "for-leaders/agentic-ai", "href": "/courses/for-leaders/agentic-ai"}, {"title": "AI Transformation", "slug": "for-leaders/ai-transformation", "href": "/courses/for-leaders/ai-transformation"}, {"title": "AI Governance", "slug": "for-leaders/ai-governance", "href": "/courses/for-leaders/ai-governance"}, {"title": "Communication", "slug": "for-leaders/communication", "href": "/courses/for-leaders/communication"}, {"title": "Influence", "slug": "for-leaders/influence", "href": "/courses/for-leaders/influence"}, {"title": "Strategy", "slug": "for-leaders/strategy", "href": "/courses/for-leaders/strategy"}, {"title": "Management", "slug": "for-leaders/management", "href": "/courses/for-leaders/management"}, {"title": "People Operations", "slug": "for-leaders/people-operations", "href": "/courses/for-leaders/people-operations"}, {"title": "Exec Presence", "slug": "for-leaders/executive-presence", "href": "/courses/for-leaders/executive-presence"}, {"title": "Storytelling", "slug": "for-leaders/storytelling", "href": "/courses/for-leaders/storytelling"}, {"title": "Goal-setting", "slug": "for-leaders/goal-setting", "href": "/courses/for-leaders/goal-setting"}, {"title": "Personal Brand", "slug": "for-leaders/personal-brand", "href": "/courses/for-leaders/personal-brand"}, {"title": "Career Growth", "slug": "for-leaders/career-growth", "href": "/courses/for-leaders/career-growth"}]}, {"category": {"slug": "founders", "href": "/courses/founders", "title": "Founders", "palette": {"--palette-category-base": "#0C3209", "--palette-category-highlight": "#077202", "--palette-category-border": "#69C662"}}, "topics": [{"title": "AI for Founders", "slug": "for-founders/ai", "href": "/courses/for-founders/ai"}, {"title": "Agentic AI", "slug": "for-founders/agentic-ai", "href": "/courses/for-founders/agentic-ai"}, {"title": "AI Workflows", "slug": "for-founders/ai-workflows", "href": "/courses/for-founders/ai-workflows"}, {"title": "Vibe Coding", "slug": "for-founders/vibe-coding", "href": "/courses/for-founders/vibe-coding"}, {"title": "Prototyping", "slug": "for-founders/prototyping", "href": "/courses/for-founders/prototyping"}, {"title": "Product Sense", "slug": "for-founders/product-sense", "href": "/courses/for-founders/product-sense"}, {"title": "Positioning", "slug": "for-founders/positioning", "href": "/courses/for-founders/positioning"}, {"title": "Product Discovery", "slug": "for-founders/product-discovery", "href": "/courses/for-founders/product-discovery"}, {"title": "Management", "slug": "for-founders/management", "href": "/courses/for-founders/management"}, {"title": "Strategy", "slug": "for-founders/strategy", "href": "/courses/for-founders/strategy"}, {"title": "Go-to-Market", "slug": "for-founders/gtm", "href": "/courses/for-founders/gtm"}, {"title": "Personal Brand", "slug": "for-founders/personal-brand", "href": "/courses/for-founders/personal-brand"}, {"title": "Leadership", "slug": "for-founders/leadership", "href": "/courses/for-founders/leadership"}, {"title": "Fundraising", "slug": "for-founders/fundraising", "href": "/courses/for-founders/fundraising"}, {"title": "PMF", "slug": "for-founders/product-market-fit", "href": "/courses/for-founders/product-market-fit"}]}, {"category": {"slug": "more", "href": "/courses/more", "title": "More", "palette": {"--palette-category-base": "#1E2C2B", "--palette-category-highlight": "#4A6361", "--palette-category-border": "#B9C1BE"}}, "topics": [{"title": "Everyone", "slug": "for-everyone", "href": "/courses/for-everyone"}, {"title": "Operators", "slug": "for-operators", "href": "/courses/for-operators"}, {"title": "Data Scientists", "slug": "for-data-scientists", "href": "/courses/for-data-scientists"}, {"title": "Business Analysts", "slug": "for-business-analysts", "href": "/courses/for-business-analysts"}, {"title": "User Researchers", "slug": "for-user-researchers", "href": "/courses/for-user-researchers"}, {"title": "Customer Success", "slug": "for-customer-success", "href": "/courses/for-customer-success"}, {"title": "Project Managers", "slug": "for-project-managers", "href": "/courses/for-project-managers"}, {"title": "HR Professionals", "slug": "for-hr-professionals", "href": "/courses/for-hr-professionals"}, {"title": "Sales People", "slug": "for-sales-people", "href": "/courses/for-sales-people"}, {"title": "Lawyers", "slug": "for-lawyers", "href": "/courses/for-lawyers"}, {"title": "Finance", "slug": "for-finance-professionals", "href": "/courses/for-finance-professionals"}, {"title": "Investors", "slug": "for-investors", "href": "/courses/for-investors"}, {"title": "Real Estate", "slug": "for-real-estate-agents", "href": "/courses/for-real-estate-agents"}, {"title": "Educators", "slug": "for-educators", "href": "/courses/for-educators"}, {"title": "Creators", "slug": "for-creators", "href": "/courses/for-creators"}]}], "navTagLinks": [{"tag": {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}, "link_slug": "for-engineers/rag-search"}, {"tag": {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, "link_slug": "for-engineers"}, {"tag": {"id": 299, "tag_type": "persona", "label": "For Data Scientists", "slug": "for-data-scientists", "description": null, "parent_tag_id": null}, "link_slug": "for-data-scientists"}], "now": "2026-05-19T22:53:50.746Z"}, "__N_SSG": true}, "page": "/p/[slug]/[...ignored]", "query": {"slug": "e73bd3", "ignored": ["build-a-document-processing-pipeline-for-rag-systems"]}, "buildId": "qPlLjBFLQ9fnkFKJ7n7E-", "isFallback": false, "isExperimentalCompile": false, "gsp": true, "scriptLoader": []}}