Handling Context Window Drift

The Problem with Context

Large language models operate within a fixed context window. This is the amount of data the model can see at any given moment. When a document exceeds this limit, the model starts to drop earlier information. This is called context window drift. You might notice that Pip stops referencing your notes from page one when you are working on page ten. We do not use magic to fix this. We use a sliding window cache.

Implementation of the Buffer

We categorize your input into three segments. The first segment is your prompt. The second is the current active section. The third is a dynamic summary of previous interactions. We strip out conversational filler and focus on key terms. This compressed representation stays in the top-level buffer. It allows Pip to maintain a factual baseline while focusing processing power on your current query. This reduces the frequency of hallucinations regarding past course material.

Managing Latency vs Accuracy

There is a cost to keeping history active. Frequent re-summarization increases latency. We chose to prioritize accuracy over immediate responsiveness. When you upload a new document, the system initiates a background indexing process. You may see a slight delay while the model maps the document structure. This ensures that the vector database is updated before you start querying. We prefer a slow start over a wrong answer.

Future Adjustments

We are testing a new tiered memory system. It assigns different weights to different parts of a textbook. Definitions and formulas get higher priority in the summary buffer than headers or examples. This should help Pip hold onto core concepts longer as you work through chapters. We will deploy this to the beta channel next week. You can monitor the status on the status page. If the system drops context during your next session, refresh the cache using the sidebar menu.