AI-Powered Search authors Trey Grainger and Doug Turnbull are excited to kick off the second cohort of their immersive virtual course on AI Search next week, running March 5 to April 3rd, 2026. Learn live with experts and peers from top companies like Pinterest, AirBnb, Apple, Uber, Amazon, Best Buy, and more!

Enroll now ➡️: AI-Powered Search: Modern Retrieval for Humans & Agents Course

We’re also offering a FREE “Exploring Modern AI Search” Lightning Lesson Series (6 complimentary sessions) in the week leading up the the course, which will give you a sneak peak into our teaching style and the topics we cover in the course.

The Course

AI Search is one of the most in-demand skill sets in AI right now. If you’re worried about your career, this course is a high-value return on investment.

The AI-Powered Search: Modern Retrieval for Humans and Agents course is hands-on (with labs on most major topics), features many industry leading guest instructors (see below), and delivers multiple live class sessions with Doug and Trey every week. It routinely makes the top courses list on Maven.com.

All sessions (about 22 hours in total) will include live learning along with students from many top companies like Uber, AWS, Airbnb, Pinterest, Shipt, Wayfair, Shipt, Doordash, and more. Doug and Trey will also hold weekly office hours available exclusively to students in the course.

While you’re encouraged to attend live to maximize your learning opportunity, all students will have permanent access to all course recordings, the code / notebooks, slides, and a dedicated community channel with other students and with the instructors.

Student Reviews: reviews.aipowedsearch.com

Expert Instructors at the Frontier of AI Search

The course is PACKED with leading experts in the field, helping you understand and put into practice the latest and greatest in AI Search techniques.

Course Leads

Guest Instructors

Complimentary “Exploring Modern AI Search” Lightning Lesson Series

In the lead up to the course, Trey and Doug are hosting a series of FREE Lightning Lessons with top search experts. (By attending a Lightning Lesson, you’ll also get access to a promo code for a course enrollment discount). You can attend live, and by signing up you’ll also get access to the recordings of all sessions.

Enroll now ➡️: Exploring Modern AI Search Lightning Lesson Series (6 Sessions)

Here’s the list of sessions included:

You can find these and many more previous Lightning Lessons here.

If you like this kind of content, please support Doug and Trey by enrolling in the full course, where you’ll get comprehensive and hands-on experience and significantly improve your AI Search skills to build better, more relevant search for people and agents.

Course Syllabus

The full syllabus can be found on the course sign up page, but the high-level course topics are summarized below:

Week 1

Mar 5 – Mar 6

• Course Overview + The Search Relevance Problem
  • 📄 Welcome & Course Overview
  • 📄 Language Models, Knowledge Graphs, and Knowledge Representation
  • 📄 Matching & Ranking
  • 📄 Lexical search
  • 📄 Embeddings & vector search
  • 📄 Dimensions of user intent
• (Optional) Vector Search Fundamentals: Vector Spaces, Matching, & Ranking
  • Course Prerequisite / Background material
• Office Hours / Initial Code Setup

Week 2

Mar 10—Mar 13

• Behavioral Signals & Reflected Intelligence Models
  • 📄 Collecting Proper Clickstream Data
  • 📄 Signals Boosting Models
  • 📄 Personalized Search & Behavioral Embeddings
  • 📄 Collaborative Filtering
  • 📄 Knowledge Graph Learning
  • 📄 Machine Learned Ranking (LTR) Intro
• Measuring Search Relevance with User Behavior [Daniel Wrigley]
• AI Search Modalities: Multimodal, Single vs. Multivector, Hybrid Search (and everything in between)
  • 📄 Bi-encoders vs. Cross-encoders
  • 📄 Multimodal Search
  • 📄 Hybrid Search
  • 📄 Personalized Search
  • 📄 Late Interaction (Colbert, ColPali, etc.)
  • 📄 Combining Query Modalities
• Office Hours
• Building Ranking Classifiers / Learning to Rank (LTR)

Week 3

Mar 17—Mar 20

• Building Ranking Classifiers / Learning to Rank (LTR)
  • Creating great training data
  • 📄 Popular classic models and how they work
  • 📄 Feature engineering
  • 📄 Building and deploying a model end-to-end
• MUVERA for Efficient Multivector Search [Connor Shorten]
• Modern RAG (Retrieval Augmented Generation)
  • 📄 Naive RAG
  • 📄 Optimal Chunking Strategies
  • 📄 Agentic & Adaptive RAG
  • 📄 Specialized RAG Techniques (GraphRAG, etc.)
  • 📄 Guardrails
• Office Hours

Week 4

Mar 24—Mar 27

• Automating LTR with Click Models and Active Learning
  • 📄 Building click models
  • 📄 Overcoming ranking biases
  • 📄 Active learning
  • 📄 End-to-end Automated LTR
• Improved Vector Search: Wormhole Vectors, Quality, & Performance Optimizations
  • 📄 Wormhole vectors
  • 📄 Pooling & Manipulating Semantic Meaning of Vectors
  • 📄 Sparse Lexical Expansion Techniques
  • 📄 Traversing disjoint vector spaces (dense semantic, sparse lexical, behavioral)
  • 📄 ANN Strategies
  • 📄 Quantization / Representation Learning
  • 📄 Scalar & Binary Quantization
  • 📄 Product Quantization
  • 📄 Matryoshka Representation Learning
  • 📄 Reranking & Recall Optimization
  • 📄 Combining Optimization Strategies
• Optimizing & fine-tuning embedding models (with your data) [Radu Gheorghe]
• Office Hours

Week 5

Mar 30—Apr 3

• Query Understanding: What’s really in your bag of documents? [Daniel Tunkelang]
• Implementing Semantic Query Understanding
  • 📄 Semantic Query Parsing
  • 📄 “Bag of Documents” approaches
  • 📄 Semantic Knowledge Graphs
  • 📄 Query Classification (index-based)
  • 📄 Query-sense disambiguation
  • 📄 Semantic Caching
  • 📄 Implementing Wormhole Vectors
• Agentic Search
  • 📄 The Agentic Search paradigm
  • 📄 Query classification (LLM-based)
  • 📄 Query sense disambiguation (LLM-based)
  • 📄 Optimizing search for humans vs. Agents
  • 📄 Search as a series of tool calls for Agent coordinators
  • 📄 Cheating at Search with Agents
• Final Office Hours & Farewell

How Does the Course Differ From the Book?

One of the top questions we get about the course is how it differs from the AI-Powered Search book. The short answer is there’s about a 30% overlap in content. The course covers a lot of material that is newer and outside the scope of the book, and the course is also a much more engaging and impactful way to learn these topics (live with Doug, Trey, and a cohort of peers). So the two complement each other very well. Here’s Dmitry Kan asking Trey about this during their recent “Wormhole Vectors” Lightning Talk.

Enrollment Deadline

Class starts on Mar 5, but you can
save 20% if you sign up by 2/28 with code “luckytiming“
save 15% ($270 off) if you sign up by 3/3 with code “lastchance“
save $250 if you sign up by 3/4 with code “justintime“
LATE ENROLLMENT: save $150 with code “catchup” until registration closes PERMANENTLY on March 11th @ 12pm EDT.

(also, be sure to ask your employer if they have some training budget for 2026 to assist you.)

Don’t miss out on this amazing opportunity to hone your skills for building modern retrieval for humans and agents!