Best Practices for RAG Implementation in Production

## Introduction to Production RAG

Building a RAG system that works in development is one thing. Building one that scales in production is another challenge entirely. In this guide, we'll cover the essential best practices we've learned from deploying RAG systems for hundreds of enterprise customers.

### Chunking Strategy

The way you split your documents has a massive impact on retrieval quality.

**Fixed-size chunks** are simple but often break context:
```python
# Not recommended for production
chunks = [text[i:i+512] for i in range(0, len(text), 512)]
```

**Semantic chunking** preserves meaning:
```python
# Better approach
from notir import SemanticChunker

chunker = SemanticChunker(
max_chunk_size=512,
overlap=50,
preserve_sentences=True
)
chunks = chunker.chunk(document)
```

### Embedding Optimization

Choose your embedding model wisely:

| Model | Dimensions | Speed | Quality |
|-------|------------|-------|---------|
| OpenAI ada-002 | 1536 | Fast | Good |
| Cohere embed-v3 | 1024 | Fast | Better |
| BGE-large | 1024 | Medium | Best |

### Caching Strategies

Implement multi-level caching:

1. **Query cache**: Store frequent query results
2. **Embedding cache**: Don't re-embed unchanged documents
3. **Result cache**: Cache final responses for identical queries

### Monitoring and Observability

Track these key metrics:

- Query latency (p50, p95, p99)
- Retrieval accuracy (manual sampling)
- Cache hit rates
- Document freshness

### Conclusion

Building production RAG systems requires careful attention to chunking, embedding selection, caching, and monitoring. Start with these best practices and iterate based on your specific use case.