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- 2 Eki 2026
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You build a skill that produces page optimization briefs and it works. A couple of months later, however, the output is noticeably worse. Maybe the briefs are more vague, or they mention services you no longer offer.
When this happens, we might blame a model update or try to fix our prompts. But if the prompt has worked consistently across models, we need to turn our attention to the context the LLMs use to generate output.
By addressing context issues and creating a system to identify and cull rot, you can improve your Claude outputs.
Context is everything an LLM reads while it tries to complete a task. While some of the context is provided via the prompt, that’s not the only context Claude and other AI tools rely on.
In addition to any background you shared in your prompt or attached files, context also includes the skills Claude uses, its memory, project knowledge, whatever a tool returns when it runs, what an earlier stage handed forward, and earlier turns of a long thread.
Often, this material loads without anyone asking for it. You didn’t type it into the prompt or point the prompt at it, so there’s no moment in your process where you’d think to review it. Managing that material on purpose is what people mean by context engineering.
Anthropic’s engineering team calls context “the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference.” Unfortunately, it’s easy to do the curating once, when you build the skill, and never get back to the maintenance.
Context rot is what happens when the material a job reads degrades over time. The workflow stays the same while the work around it changes:
You add or drop a service, and the skill still describes the old offer
Your company restructures, or your team changes how it works
A tool you rely on changes what it returns or how it formats it
Your style guide gets revised, and the old rules are still in the files the job reads
When this happens, we might blame a model update or try to fix our prompts. But if the prompt has worked consistently across models, we need to turn our attention to the context the LLMs use to generate output.
By addressing context issues and creating a system to identify and cull rot, you can improve your Claude outputs.
Context is everything an LLM reads while it tries to complete a task. While some of the context is provided via the prompt, that’s not the only context Claude and other AI tools rely on.
In addition to any background you shared in your prompt or attached files, context also includes the skills Claude uses, its memory, project knowledge, whatever a tool returns when it runs, what an earlier stage handed forward, and earlier turns of a long thread.
Often, this material loads without anyone asking for it. You didn’t type it into the prompt or point the prompt at it, so there’s no moment in your process where you’d think to review it. Managing that material on purpose is what people mean by context engineering.
Anthropic’s engineering team calls context “the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference.” Unfortunately, it’s easy to do the curating once, when you build the skill, and never get back to the maintenance.
Context rot is what happens when the material a job reads degrades over time. The workflow stays the same while the work around it changes:
You add or drop a service, and the skill still describes the old offer
Your company restructures, or your team changes how it works
A tool you rely on changes what it returns or how it formats it
Your style guide gets revised, and the old rules are still in the files the job reads