Keeping LLM Answers Accurate When Source Material Overflows the Context Window

A generative model in your product has to work with inputs far longer than its context window can hold. How would you handle or condense that material so the model still gives good results? ```hint Start from the task What the model must do with the input decides what can be left out. Answering a narrow question, summarizing everything, and carrying on a long conversation each tolerate a different kind of loss, so pin the task down before you pick a technique. ``` ### Clarifying Questions - What is the long input: one very large document, a big collection of files, or a conversation history that keeps growing? - Does the task need the whole input, as in a full summary or a contract review, or only the parts that bear on a specific question? - What latency and cost per request can the product afford, and may inputs be processed ahead of time, before anyone asks? - Is the model fixed, or could we move to one with a larger window? ### What a Strong Answer Covers - Matching the approach to the task: targeted lookup, whole-input synthesis, or a long-running conversation. - What each approach loses (missed passages, errors that compound across layers of summaries, forgotten early turns) and how the design limits that loss. - The cost and latency trade-off between sending everything to a long-context model, making many smaller calls, and preprocessing offline. - How quality is measured, including facts placed deep inside the input and whether answers stay faithful to it. ### Follow-up Questions - A new model offers a window ten times larger at a higher price per token. How do you decide whether it should replace your pipeline? - Users say answers ignore details from the middle of long documents. How would you diagnose and fix that? - How would the assistant recall something a user mentioned months ago without resending the whole history each time?