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The Problem: Traditional search training focuses on outdated techniques.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 Implementation

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 need—not just what they type.

📊 Harness Your Users' Intelligence

Your 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 Works

Retrieval 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 Models

Move 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 Strategies

Bridge the gap between prototype and production with ANN optimization, quantization strategies, semantic caching, and model serving architectures that scale to millions of users.

The Transformation You'll Experience

Week 1: Foundation & Behavioral Intelligence

Master the core search relevance problem and discover how to automatically improve rankings using your users' own behavior patterns—techniques that powered Wikipedia's search overhaul.

Week 2: Modern AI Integration

Learn when to use bi-encoders vs. cross-encoders, implement multimodal search, leverage modern late interaction models instead of out-of-the-box vector search, and build ranking classifiers that learn from real user feedback—not just static training data.

Week 3: Automated Machine-learned Ranking & RAG Strategies

Automate your learning-to-rank systems with click models and active learning, plus master the retrieval strategies that make RAG actually work in enterprise environments.

Week 4: Productionizing AI Search

Optimize everything for production scale: quantization, ANN strategies, semantic caching, and the query intent mastery that lets you "cheat" at search using LLMs strategically.

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 Line

This 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 5th, 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?