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12

RAG & Vector Databases

> augment_knowledge_efficiently()

Master Retrieval-Augmented Generation and vector databases. Learn chunking strategies that actually work, when to use RAG vs fine-tuning, and how to build a production codebase knowledge base.

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Expansion Guides

// from naive retrieval to intelligent knowledge systems

01

Chunking Strategies That Actually Work

From naive splitting to semantic chunking

Chunking makes or breaks RAG. Learn why character-count chunking fails, how to preserve semantic meaning, and the strategies that improve retrieval quality by 40%.

Semantic Chunking Context Preservation Overlap Strategy Code Chunking
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02

When to Use RAG vs Fine-Tuning

Decision matrix for knowledge augmentation

RAG and fine-tuning solve different problems. Learn when to retrieve, when to teach, when to combine both, and the cost-performance tradeoffs of each approach.

RAG vs Fine-tune Cost Analysis Update Frequency Hybrid Approach
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03

Building a Codebase Knowledge Base

Production-ready code RAG implementation

Turn your codebase into a queryable knowledge base. Learn file prioritization, dependency graph embedding, incremental updates, and the architecture patterns that make it production-ready.

Code Embedding AST Parsing Dependency Graph Incremental Update
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