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The future of search is agentic. Are you ready?
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.
What you’ll learn
Rethink search to put tireless agents at work for human users
Build search backends agents can reason about
Why agents love simplicity over complexity
Focusing tools on domain tasks, not "search"
Sidestep complex NLP to better understand queries
Iterate on query understanding through agentic approaches
Organize and structure content to be retrievable by an agent
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)
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
Who this course is for
Search technologists that want to learn how to ensure RAG and agentic search win
Startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices
Data scientists that want to consider how agentic / LLM / RAG based search can be oriented towards user feedback and business outcomes
Prerequisites
Lexical Search basics (do you know what BM25 is)
We will build on top of existing, core search knowledge
Basic search evaluation (judgments + metrics)
We will use common search datasets that use metrics like NDCG, etc to measure search
Vector search basics (do you know what an embedding is?)
Vector search will be one tool in our toolkit we'll apply to the agentic tools that we build
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
Your purchase is backed by the Maven Guarantee.
Course syllabus
Week 1
May
18
Lesson 1 - Information Retrieval as an agentic process
May
19
Max Rumpf - Sid.ai - fine tuning an agentic search LLM
May
20
Lesson 2 - LLM Query Understanding
May
21
Matt Overstreet - IBM - PageIndex + OpenSearch
May
22
Office Hours
Week 2
May
25
Lesson 3 - An agentic harness - that searches
May
27
Lesson 4 - Beyond LLM as a judge
May
29
Office Hours
May
28
Guest Speaker - Antoine Chaffin - building late interaction models
Free resource
Cheat at Search Essentials: Vectors and Embeddings
Learn how embeddings implement 'semantic search'
What is a 'vector embedding' and why do they capture meaning? And how can that be used to build a search system?
Tackle the practical realities vector retrieval
Why do we need a vector database? And why are they different from traditional search engines? Should they be different?
How are vector + lexical techniques combined?
Offer a concrete and concise explanation of how you will help students understand and apply this lesson.
Schedule
Live sessions
2-4 hrs / week
Three hours of courses. Optional guest talks from industry experts every week. Office hours to deep dive into your problems with your instructor
Mon, May 18
5:00 PM—6:30 PM (UTC)
Tue, May 19
4:00 PM—5:00 PM (UTC)
Wed, May 20
5:00 PM—6:30 PM (UTC)
Projects
1-2 hrs / week
Optional labs to expand your knowledge and deliver into your team's codebase
Why agents completely change search+RAG
Walking through exactly why / how agents change the game for search+RAG
How to eval retrieval for RAG
Hamel Hussain + Doug Turnbull discuss RAG evaluation
Frequently asked questions
$1,450
USD
5 days left to enroll