Let's start with a hard truth: Most retrieval augmented generation (RAG) implementations fail to make it out of the proof-of-concept stage. A rencet g

RAG Best Practices: Lessons from 100+ Technical Teams

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2024-11-11 08:30:02

Let's start with a hard truth: Most retrieval augmented generation (RAG) implementations fail to make it out of the proof-of-concept stage. A rencet global survey of 500 technology leaders shows that more than 80% of in-house generative AI projects fall short. But it doesn't have to be this way.

At kapa.ai, we've worked with over 100 technical teams like Docker, CircleCI, Reddit and Monday.com to implement RAG-based systems in production. Below is what we've learned about making it past the proof-of-concept stage.

Before diving what we've learned, here's a quick primer on RAG. Think of it as giving an AI a carefully curated reference library before asking it questions. Instead of relying solely on its training data (which can lead to hallucinations), RAG-based systems first retrieve relevant information from your knowledge base, then use that to generate accurate answers. It's like the difference between asking someone to recall a conversation from memory versus having them reference the actual transcript.

In practice, this means indexing your knowledge in a vector database—think super-powered search engine—and connecting it to large language models that can use this information to answer questions naturally and accurately. This approach has become the go-to method for building reliable AI systems that can discuss your specific product or domain.