Why most RAG systems fail in production

Most teams reach for a bigger model when retrieval quality is bad. That's rarely the fix.

The real bottleneck is chunking

If your chunks split a definition from its context, no model downstream can recover the missing half. Before touching the model:

• Chunk by semantic boundary, not fixed token count
• Keep headers/section titles attached to their body text
• Overlap chunks by 10-15% so edge context isn't lost

Retrieval accuracy over model size

A well-tuned retrieval pipeline on a mid-size model consistently beats a frontier model bolted onto sloppy chunking. Measure retrieval precision/recall directly — don't just eyeball final answers.

What to fix first

1. Re-chunk with structure awareness
2. Add a reranker before the top-k cutoff
3. Only then consider swapping the generation model

Get the retrieval layer right and the rest gets a lot easier.