Great overview of RAG and its role in building more reliable AI applications. One thing we've noticed is that success with RAG depends just as much on the quality of retrieval, chunking strategy, and keeping the knowledge base up to date as it does on the choice of LLM.
As more organizations move toward enterprise AI, understanding how RAG performs in real-world applications is becoming just as important as understanding its strengths. We've also been exploring topics around LLM evaluation and the factors that influence the reliability of AI applications.
Looking forward to seeing more perspectives on this.
Great overview of RAG and its role in building more reliable AI applications. One thing we've noticed is that success with RAG depends just as much on the quality of retrieval, chunking strategy, and keeping the knowledge base up to date as it does on the choice of LLM.
As more organizations move toward enterprise AI, understanding how RAG performs in real-world applications is becoming just as important as understanding its strengths. We've also been exploring topics around LLM evaluation and the factors that influence the reliability of AI applications.
Looking forward to seeing more perspectives on this.