Case Facts and Context Placement
Understand how to maintain reliable AI responses by correctly anchoring case facts and placing different information categories in the context window. Learn to prevent context drift, implement verbatim blocks for key data, and decide which outputs to trim or externalize, ensuring consistent accuracy in long-running AI tasks.
Context management begins with a simple question: What must Claude get right every single time, and where does that information sit in the context window? In a short session, the answer almost does not matter; Claude will read everything. In a long-running investigation, a document extraction pipeline, or a multi-step support case, context drifts. Stable facts get paraphrased, approximated, or buried under later outputs. The values Claude uses in step 12 may differ subtly from the values established in step two, not because Claude misread them, but because they were positioned where they could be crowded out.
This lesson covers how to anchor facts that must stay verbatim, how to choose where different categories of information belong in the context structure, and how to identify verbose outputs that can be trimmed or externalized without losing the signal that matters. By the end of this lesson, we will be able to:
Explain why context drift happens and why position in the context window affects fact retention.
Define a case facts block and identify what belongs ...