July 2026

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Designing and optimizing the context window for LLM applications — prompt structure, memory, and information retrieval.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Building retrieval-augmented generation and search systems — vector databases, embeddings, ranking, and retrieval pipelines.

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

AI Search Expert | Founder | Author

Lucidworks

Presearch

Optimized AI Conference

CareerBuilder

Searchkernel

Trey Grainger

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

Principal Search Engineer | Author

Shopify.com

OpenSource Connections

Reddit

Wikipedia

Yelp

Doug Turnbull

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

AI-Powered Search: Modern Retrieval for Humans & Agents

Most search implementations 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 hundreds of the Fortune 500. Why This Course Exists The Problem: Traditional search training focuses on outdated techniques. 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: Learn from practitioners who've built search systems handling hundreds of millions of queries daily. Doug and Trey have architected search for Reddit, Shopify, Wikipedia, Presearch, CareerBuilder, and dozens of Fortune 500 companies. Now they're sharing the exact methodologies that transformed these platforms. What Makes This Different 🧠 Go Beyond Surface-Level AI ImplementationStop 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 need—not just what they type. 📊 Harness Your Users' IntelligenceYour clickstream data is a gold mine of untapped potential. We'll show you how to properly collect click stream data and how to automatically improve your ranking models using "reflected intelligence"—letting user behavior continuously optimize your search without manual intervention. 🤖 Master RAG That Actually WorksRetrieval Augmented Generation is exploding, but most implementations fail at the "retrieval" part. Learn the advanced chunking strategies, adaptive querying, and guardrails that separate production-ready RAG from toy demos. 🎯 Build Production-Ready Ranking ModelsMove beyond basic relevance scoring. Implement battle-tested algorithms from LambdaMART to cross-encoders, with hands-on experience in feature engineering, click model automation, and active learning systems. 🔧 Production Optimization StrategiesBridge the gap between prototype and production with ANN optimization, quantization strategies, semantic caching, and model serving architectures that scale to millions of users. What You'll Walk Away With Immediately Applicable Skills:- Query intent classification and semantic query parsing- Click-stream analysis and automated ranking improvements- Production-ready RAG implementations with proper guardrails- End-to-end machine learning ranking pipelines- Advanced vector search optimization techniques- Combining query modalities for personalized, graph-based, multimodal, and hybrid search. Strategic Understanding:- When to use different AI search approaches (and when not to)- How to systematically improve search relevance using clickstream data- Production deployment strategies for AI search systems- The mental models that tie modern information retrieval together Career Impact:- Skills used by senior search engineers at top tech companies- Proven methodologies from instructors who've built search for hundreds of millions of users- A portfolio and code for production-ready search implementations- Direct access to a community of search professionals The Bottom LineThis isn't another AI course that teaches you to call APIs. It's not a search course stuck in 2010. It's the bridge between cutting-edge AI and production search systems that actually work. You'll learn the exact techniques used by companies processing billions of queries, taught by the engineers who built them. By December 6th, you'll have the skills to architect, implement, and optimize AI-powered search systems that deliver the intelligent AI Search experiences users expect in 2026 and beyond. Ready to transform your search capabilities?

Level up your Search & RAG skills with leading AI Search experts

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

Software engineers looking to learn both the engineering and data science side of "retrieval" needed fo modern AI systems.

Who is this course for

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

Basic Python Programming

What you’ll get out of this course

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.

Go beyond the query to understand intent

Leverage "reflected intelligence" from your use behavioral signals to automatically improve your ranking models. You'll learn to implement algorithms for signals boosting, matrix factorization, collaborative filtering, personalized search, and knowledge graph learning.

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

RAG represents the largest growing use case for search, and "retrieval" is by far the hardest part of RAG. Learn to optimize search for LLMs and agents, as well as the critical strategies (chunking, adaptive queries, guardrails, etc.) and access patterns needed to optimize RAG.

Optimize your Retrieval Augmented Generation (RAG) implementations

Learn common ranking models put into production from LambdaMART to cross-encoders

Build machine-learned ranking models from scratch

Learn how we systematically develop great ranking features using a combination of vector and lexical search indices. Generate automatic judgments from user click stream data and uncover unexplored ranking features leveraging simplified dynamic bayesian networks (SDBNs).

Develop effective ranking features and automate improvements with click models and Active Learning

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, ANN vs. quantization vs. representation learning, pooled vectors vs. late interaction vs. late chunking, etc.

Fill in holes in your modern information retrieval knowledge

Get hands on experience optimizing AI Search for production. Implement ANN and quantization strategies, reranking, semantic caching, and even local model serving for search and RAG.

Operationalize AI Search

Go back to course content and recordings (~20 hours) whenever you need to.

Lifetime access

Experts from OpenSource Connections, Qdrant, Max.io, OpenSearch, and Superlinked will present special guest sessions on bleeding-edge topics

Expert Guest Instructors

Stay accountable and learn with like-minded professionals from top companies Uber, AWS, Airbnb, Yahoo, Yelp, Wayfair, Shipt, Doordash, etc.

Community of peers from Top Companies

Have access to dedicated time every week with the instructors to dive into your AI Search challenges, get assistance with labs, and more.

Office Hours with Doug & Trey

Share your new skills with your employer or on LinkedIn.

Certificate of completion

Course syllabus

Freelance software and business consultant

What people are saying

Meet your instructors

Tuesday: Live Course Session (Trey/Doug)

We're bringing in subject-matter experts for specialized sessions covering several emerging topics at the frontier of AI-powered search.

Wednesday: Guest Speaker

Thursday: Live Session (Doug/Trey)

Check in bring your questions to discuss with Trey and Doug.

Fridays - Office Hours

Jupyter-notebooks-based assignments to sharpen your skills and reinforce course material.

Notebook-based Labs

Typical Weekly Schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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About this course

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