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Learning Series

RAG Done Right

A 4-part series to help you master this topic step by step.

Technical Deep Dives/Retrieval & DataTechnical Deep Dives/Patterns in Practice
4 parts in this series
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AI

Series Outline

1

RAG vs Fine-tuning: Which Approach Should You Use?

The most common AI architecture decision: RAG or fine-tuning? A practical framework for choosing the right approach based on your use case, data, and constraints.

Tom HundleyTom Hundley
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2

Chunking Strategies for RAG: What Actually Works

Chunking makes or breaks RAG performance. Compare strategies—fixed-size, semantic, and recursive—and learn how to optimize chunk size and overlap for your use case.

Tom HundleyTom Hundley
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3

Embeddings Deep Dive: Understanding Vector Representations

Embeddings are the foundation of semantic search and RAG. Understand how they work, compare popular models, and learn how to choose the right one.

Tom HundleyTom Hundley
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4

Production RAG Architecture: Scaling Retrieval Systems

Move from RAG prototype to production. Architecture patterns for scale, hybrid search approaches, caching strategies, and hard-won lessons from real deployments.

Tom HundleyTom Hundley
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Ready to start learning?

Begin with Part 1 and work your way through the series at your own pace.

Start with Part 1