Retrieval-augmented generation (RAG) is 2024’s “LAMP-stack moment” - suddenly everyone can bolt a vector DB onto an LLM and start answering questions over private data.
But 2025 research is already pushing far past vanilla “top-k chunk + concat + generate.”
This post maps the next waves, explains why GitHub stars alone won’t keep you bleeding-edge, and shows where to point your radar instead.
1. Why plain RAG is hitting a ceiling
- Shallow recall - vectors often miss causal chains, tables, multi-hop logic.
- Static context - once the top-k passages are stuffed into the prompt, the system can’t adapt mid-conversation.
- Operational drag - chunk-size tuning, vector-DB ops, and eval loops still demand manual craft.
The next generation layers reasoning, memory, agents,…
