Cheat at Search with Agents
Doug Turnbull
Led Search Reddit + Shopify. Wrote Relevant Search + AI Powered Search
Your guide to agentic search
Achievable, Relevant Search without the PhD
Search relevance once took months of dedicated expertise to see results. With LLMs and agents, you can see wins within weeks or days. In this course go from LLM query classification, to agentic search optimization, to code generation for relevance reranking
What you’ll learn
Learn to build LLM based query + content understanding in search
Build search backends agents can reason about
Why agents love simplicity over complexity
Sidestep complex NLP to better understand queries
Iterate on query understanding through agentic approaches
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)
Letting an agent using its judgments to iterate on relevance on its own
Integrating external feedback (user clicks, etc) into agentic relevance
Learn directly from Doug
Doug Turnbull
Doug Turnbull
Led Search Reddit + Shopify. Wrote Relevant Search + AI Powered Search
Who this course is for
Search teams eager to evolve that crufty-old search engine towards a modern approach
Search startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices
Team leads and managers excited to organize their work in a way aligned with modern approaches to Relevant Search
What's included
Live sessions
Learn directly from Doug Turnbull in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Syllabus
Week 1
Homework
Week 2
Featured lesson
Build a vector database from scratch
Learn how embedding similarity search works
Learn the most common graph-based vector search algorithm used in Elasticsearch, Weaviate, QDrant, Pinecone, etc
See where vector search goes wrong
As you watch an expert live-code, see where the algorithm falls apart as a real-life expert makes a mistake
Deepen your vector search knowledge
Dense vectors provide specific constraints on retrieval solutions - learn what these are, how they can go wrong
Schedule
Live sessions
Projects
Async content