Improving RAG Systems with PageIndex

Retrieval-Augmented Generation (RAG) has quickly become one of the most practical ways to build AI applications on top of custom data. From documentation assistants to internal company knowledge bots, RAG enables large language models to answer questions using external information instead of relying purely on training data. But once your dataset grows beyond a few documents, something frustrating starts happening: The model begins returning incomplete or confusing answers. Often the issue isn’t the LLM itself — it’s retrieval quality. One simple idea that can dramatically improve RAG pipelines is PageIndex. The Hidden Problem with Traditional RAG Most RAG pipelines follow a similar workflow: Documents are split into chunks Each chunk is converted into embeddings Embeddings are stored in a vector database At query time, the system retrieves the most similar chunks Those chunks are passed to the LLM as context This approach works well initially. But it has a structural weakness. Chunks l