## Introduction
RAG (Retrieval-Augmented Generation) has become the go-to pattern for building AI systems that work with your data. But it's also easy to get wrong.
## Mistake #1: Poor Chunking Strategy
The way you split documents matters enormously. Too small, and you lose context. Too large, and you retrieve irrelevant content.
**Fix:** Experiment with chunk sizes. Use semantic chunking when possible. Preserve document structure.
## Mistake #2: Ignoring Metadata
Retrieval isn't just about content—it's about context. When was this written? By whom? For what purpose?
**Fix:** Enrich chunks with metadata. Use it in retrieval and generation.
## Mistake #3: No Fallback Strategy
What happens when retrieval finds nothing relevant?
**Fix:** Design graceful degradation. Know when to say "I don't know."
## Mistake #4: Skipping Evaluation
How do you know your RAG system is actually working?
**Fix:** Build evaluation sets. Measure retrieval and generation quality separately.
## Mistake #5: Forgetting About Updates
Documents change. How does your RAG system stay current?
**Fix:** Design for continuous ingestion. Version your embeddings.
## Conclusion
RAG is powerful, but only when implemented thoughtfully. Avoid these mistakes to build systems that actually work.