AI-Powered Search

Cohort 1

Nov 2025

March-2026

July 2026

Today’s retrieval systems operate at massive scale (100 billion vectors) and are undergoing exciting AI-driven paradigm shifts to improve retrieval quality, including new approaches for Multivector, Multimodal, Hybrid, and Agentic Search. In this series, we’ll explore both the foundational principles of modern AI Search, as well as what’s being implemented at the frontier of information retrieval.

"Exploring Modern AI Search" Lightning Lesson Series (6-Sessions)

Learn the latest AI Search skills with the authors of "AI-Powered Search"

AI Search Expert | Founder | Author

Trey Grainger

Founder, Searchkernel. Author, "AI-Powered Search". 18+ years leading search.

Principal Search Engineer | Author

Doug Turnbull

Principal Search Engineer, Author of "AI-Powered Search" & "Relevant Search".

AI-Powered Search: Modern Retrieval for Humans & Agents

Staff Software Engineer

Data Scientist

ML Engineer

Generative AI Engineer

Senior Performance Analyst, Instructor

Enterprise Architect 

Data Engineer

Principal NLP Engineer

Lucidworks

Presearch

Optimized AI Conference

CareerBuilder

Searchkernel

Shopify.com

OpenSource Connections

Reddit

Wikipedia

Yelp

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 ExistsThe 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?

Level up your Agentic Search & Retrieval skills with leading AI Search experts

Optimize your Agentic Search and Retrieval Augmented Generation (RAG) systems

Go beyond the query to understand intent

Fill in holes in your modern information retrieval knowledge

Improve relevance with crowdsourced ranking models built from click-stream data

Optimize AI-Powered Search Models for Production

Build ranking features & automate judgements with click models & Active Learning

Examples will be taught in Python, it's a good idea to have basic Python familiarity under your belt

Basic Python Programming

For those new to search/retrieval, we provide an optional intro session covering core matching & ranking concepts (BM25, vector spaces, etc)

(Included Complimentary with the Course): Search Fundamentals

AI Engineers optimizing search ranking to measurably improve user search satisfaction

Search Practitioners that want to learn to systematically improve search relevance

Technology leaders hoping to plan search experiments to maximize clicks and conversions

Vector search from zero to production at GitHub

Author AI Powered Search

David Tippett

Search Relevance @ Github

Co-Author AI Powered Search

Why early vector-search MVPs can mislead you, and what to test before production.

Plan semantic search beyond the MVP

How capacity, indexing, sharding, reindexing, and Lucene merges complicate rollout.

Debug vectors at production scale

How scoring, oversampling, num_candidates, and linear retrievers affect relevance.

Tune retrieval for real users

Vectorless RAG with PageIndex

Matt Overstreet

Product Manager - IBM OpenSearch

Author, AI-Powered Search

Learn how PageIndex breaks up content up using the author's own boundaries (table of contents + section breaks)

Chunking with document structure

Matt will share how he's integrated PageIndex into IBM's OpenSearch product

Integrating PageIndex and OpenSearch

RAG implies chunks + embeddings. But navigating document structure to find what's relevant is just as important.

Solving RAG without embedddings

Cross-encoders: An End-to-end Guide

Radu Gheorghe

Software Engineer, Vespa.ai

As a form of ranking classifier, cross encoders are an essential tool for search relevance ranking.

What are cross-encoders and their uses?

Compared to regular vector search (with bi-encoders), cross-encoders greatly improve semantic ranking, but at a cost.

Trade-offs using cross-encoders vs. standard vector search

Exporting the cross-encoder model (ONNX format), tokenizing doc and query text, & connecting the dots in a rank profile.

An end-to-end walkthrough of using cross-encoders (in Vespa)

Optimization tricks like using multi-stage ranking and running the cross-encoder on a GPU.

Tips and tricks to keep latency and cost in check

Better Agentic Search with Adaptive Relevance Pipelines

Kevin Butler

Senior Search & AI Engineer at KMW Technology

Close the loop with LLM-as-judge signals, building an end-to-end adaptive search architecture

Self-Improving Pipelines

Supercharge indexing via the open source Lucille ETL framework to align your search engine and your agentic tooling.

Agentic Document Augmentation

Read signals like confidence gaps and score variance at runtime to drive quality gate retries

Live Retrieval Diagnostics

Search Trends in 2026 - by the numbers

Dawn Anderson

Founder, Bertey. SEO Consultant.

Co-Author, AI-Powered Search

Detailed analysis of emerging search trends in 2026

How do users actually search in 2026?

Is there a generational gap in AI search? In what domains should you prioritize chat-based search?

Which users use AI search functionality?

Market statistics on what users actually want out of search beyond hope and wishful thinking

Ground product planing in reality

A Bag-of-Documents Model for Query Understanding & Retrieval

Daniel Tunkelang

Independent Search Consultant. LinkedIn / Google / Endeca alum.

Learn an approach to align query and document representations in order to improve retrieval and ranking.

What is the bag-of-documents model?

Build a retrieval model by aggregating queries, results and relevance judgments; and then fine-tuning a base model.

How do you implement the bag-of-documents model?

Learn where the bag-of-documents model outperforms alternative approaches, but also where it can be tripped up.

Learn directly from AI-Powered Search