Every few months, someone declares RAG dead. "Context windows are big enough now," they say. "Just stuff everything in the prompt." And every few months, they're wrong.
Why RAG still matters
Context windows have grown — GPT-4 Turbo handles 128k tokens, Claude handles 200k. That's a lot. But it's not enough for:
- Enterprise knowledge bases (millions of documents)
- Real-time data (prices, inventory, news)
- Cost efficiency (you're paying per token)
- Auditability (you need to know where answers came from)
What's changed in 2026
RAG isn't dead, but naive RAG is. The "chunk, embed, retrieve, generate" pipeline from 2023 doesn't cut it anymore. Here's what works now:
- Hybrid retrieval: Combine vector search with keyword search. Neither is perfect alone.
- Re-ranking: Use a cross-encoder to re-rank retrieved chunks before passing to the LLM.
- Query transformation: Rewrite the user's query before retrieval. HyDE, multi-query, etc.
- Evaluation: Measure retrieval quality separately from generation quality. You can't improve what you don't measure.
The boring truth
The difference between a bad RAG system and a good one isn't the model. It's the data pipeline. Clean your data. Chunk it thoughtfully. Write good metadata. Test retrieval quality before you even touch the LLM.
Most RAG problems are data problems in disguise.
If you're building a RAG system and it's not working, the issue is probably not the model. It's probably the data. Start there.