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For CTOs & AI Engineers

Stop Feeding AI Sentence Fragments

Most AI retrieval systems chop documents into tiny pieces and hope the AI can reassemble the story. It can't.

The default RAG architecture goes like this: take your documents, chop them into 500-token chunks, embed each chunk, store the embeddings, and when the AI needs context, retrieve the top-k most similar chunks.

It works beautifully for Q&A. "What's our refund policy?" Retrieve the relevant chunk. Answer the question. Done.

Now try generating a 15-page sales proposal that references a past case study. The chunk that scores highest is 500 tokens from the middle of the case study. The client name is in a different chunk. The outcome is in another. The emotional arc that makes the reference compelling is spread across four chunks that the system doesn't know are related.

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Stop Feeding AI Sentence Fragments

Most retrieval systems chop documents into pieces and hope AI reassembles them.

The Architecture Series · 8 minute read · By Ed | Founder & CEO, DealiOS

The default RAG architecture goes like this: take your documents, chop them into 500-token chunks, embed each chunk, store the embeddings, and when the AI needs context, retrieve the top-k most similar chunks.

It works beautifully for Q&A. "What's our refund policy?" Retrieve the relevant chunk. Answer the question. Done.

Now try generating a 15-page sales proposal that references a past case study. The chunk that scores highest is 500 tokens from the middle of the case study. The client name is in a different chunk. The outcome is in another. The emotional arc that makes the reference compelling is spread across four chunks that the system doesn't know are related.

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