How to scale document question answering using LLMs

🔗 How to scale document question answering using LLMs
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At Sensible we've used large language models (LLMs) to transform documents into structured data since the developer preview of GPT-3. In that time we've developed a set of best practices for document question answering that complement the basic chunking and embedding scoring approach well-represented in frameworks like LangChain.

In particular we're focused on how to do document question answering at scale across a wide range of unknown document layouts. This differs from a scenario where you're chatting with a particular PDF and can try several prompt variants interactively to get what you want. For us, we need to create robust prompts and chunking strategies that are as invariant as possible in the face of variability between documents.

These techniques are particularly useful in mature industries where documents function as de-facto API calls between companies. We've seen customers across several verticals, including insurance, logistics, real estate, and financial services realize significant operational efficiency gains via LLM-powered document automation.

Let's dig into a few areas of optimization for document question answering with LLMs: chunking, layout preservation, cost optimization, and confidence scores.

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