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What Is the RAG Master Series?

Retrieval-Augmented Generation (RAG) has become the foundation of modern AI applications that need to access and reason about external knowledge. This comprehensive series distills years of consulting experience helping companies build, improve, and scale RAG systems in production.

RAG systems are fundamentally different from other AI applications - they combine the complexity of information retrieval with the unpredictability of language generation. This series provides a systematic approach to mastering both aspects, from basic implementations to enterprise-grade systems serving millions of users.

This guide covers everything from fundamental concepts to advanced optimization techniques, anti-patterns to avoid, and real-world case studies from successful deployments across industries.

If you want hands-on help, I recommend reaching out to my friend Nila: nila.is. Please mention you came from me.

Domain Experts: The Lever for Vertical AI — Chris Lovejoy (Anterior)

I hosted a session featuring Chris Lovejoy, Head of Clinical AI at Anterior, who shared valuable insights from his experience building AI agents for specialized industries. Chris brings a unique perspective as a former medical doctor who transitioned to AI, working across healthcare, education, recruiting, and retail sectors.

Text Chunking - Anton (ChromaDB)

I hosted a special session with Anton from ChromaDB to discuss their latest technical research on text chunking for RAG applications. This session covers the fundamentals of chunking strategies, evaluation methods, and practical tips for improving retrieval performance in your AI systems.

Why Grep Beat Embeddings in Our SWE-Bench Agent (Lessons from Augment)

I hosted Colin Flaherty, previously a founding engineer at Augment and co-author of Meta's Cicero AI, to discuss autonomous coding agents and retrieval systems. This session explores how agentic approaches are transforming traditional RAG systems, what we can learn from state-of-the-art coding agents, and how these insights might apply to other domains.

Why Cognition does not use multi-agent systems

I hosted a session with Walden Yan, co-founder and CPO of Cognition, to explore why multi-agent systems might not be the optimal approach for coding contexts. We discussed the theory of context engineering, the challenges of context passing between agents, and how single agents with proper context management can often outperform multi-agent setups.

Stop Trusting MTEB Rankings (Kelly Hong, Chroma)

I hosted a session with Kelly Hong from Chroma, who presented her research on generative benchmarking for retrieval systems. She explained how to create custom evaluation sets from your own data to better test embedding models and retrieval pipelines, addressing the limitations of standard benchmarks like MTEB.

How Extend Achieves 95%+ Document Automation (Lessons from Eli Badgio)

I hosted a special session with Eli Badgio, CTO of Extend, to discuss AI-native document processing in the cloud. Extend helps companies achieve 95%+ extraction accuracy for customers like Brex and other Fortune 500s. This session covered mapping document workflows, building task-specific evaluations, and implementing partial automation with human-in-the-loop approaches.

The 12% RAG Performance Boost You're Missing (Ayush, LanceDB)

I hosted a session with Ayush, an ML Engineer at LanceDB, to explore how fine-tuning re-rankers and embedding models can significantly improve retrieval performance in RAG systems. We discussed practical approaches to enhancing retrieval quality, the trade-offs involved, and when these techniques make the most business sense.

Why Glean Builds Custom Embedding Models for Every Customer

I hosted Manav from Glean for a guest lecture on enterprise search and fine-tuning embedding models. This session revealed a surprisingly underutilized approach that can dramatically improve RAG system performance - building custom embedding models for each customer rather than using generic solutions.

Lexical Search - John Berryman

I hosted a session featuring John Berryman, who shared his expertise on lexical search and its application in RAG systems. John, who previously worked at GitHub and co-authored books on prompt engineering and information retrieval, provided valuable insights on how traditional search techniques can complement modern vector-based approaches for more effective retrieval augmented generation.