What Building My First RAG Application Taught Me

I thought connecting an LLM to my documents would be the hard part. It wasn't. The real challenge was getting the AI to retrieve the right information, use it correctly, and admit when it didn't know.

I thought building a RAG application would be straightforward: take some documents, split them into chunks, generate embeddings, put the vectors into a database, retrieve the most relevant chunks, send them to an LLM, and get an answer. That's the diagram you see everywhere, and technically, it works — you can build a basic RAG prototype surprisingly quickly.

What nobody tells you is that getting a RAG application to give consistently useful answers is a completely different problem. The LLM is often not the hardest part. The vector database isn't the hardest part. Even prompt engineering isn't the hardest part. The difficult part is making sure the model receives the right information at...

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