This discussion reminded me of a few resources that don't get enough attention if you're building RAG or enterprise AI:
1. Self-RAG (selfrag.github.io/)
2. CRAG - Corrective RAG (arxiv.org/abs/2401.15884)
3. Microsoft GraphRAG (microsoft.github.io/graphrag…)
4. Anthropic's Context Engineering blogs (anthropic.com/engineering)
5. DeepEval (github.com/confident-ai/deep…)
6. Ragas (docs.ragas.io/)
Worth exploring if you're building beyond a basic RAG pipeline.
aditya (@adxtyahq)
Conventional enterprise AI mostly works by fetching relevant docs through RAG and feeding them to an LLM to generate an answer
Interesting approach I came across today, instead of learning from the final docs, it learns from the edits, reviews and reasoning behind them. The idea is simple, the final document tells you what happened, the edits tell you why it happened.
Interesting direction for enterprise AI
— https://nitter.net/adxtyahq/status/2070177580016443766#m