Introduction
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.
What is context?
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.
What is context rot?
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
- New files and skills get added that contradict the ones already there
- You move or rename a file, and the instructions still point to where it used to be
None of these changes reach the workflow on their own. It keeps reading the old material on every run.
6 ways context rots
Context rot shows up in six ways:
- Volume issues produce vague output that skips instructions
- Competition produces inconsistent output
- Divergence issues produce inconsistent output
- Staleness leads to inaccuracies
- Conflict produces confident inaccuracies
- Contamination introduces potential inaccuracies
Understanding the symptoms of these different types of context rot can help you identify which context to check first. If the output is more vague than it used to be or skips instructions, check how much material the job was given.
1. Volume issues produce vague output that skips instructions
Issues with volume mean there’s too much material in front of the model at once. The output gets vaguer and starts missing instructions you know are in the file.
2. Competition produces inconsistent output
Competition issues arise when there are too many valid options for the model to choose between.
3. Divergence issues produce inconsistent output
Divergence occurs when material that’s supposed to match drifts apart.
4. Staleness leads to inaccuracies
Simply put, staleness is material that was accurate when you wrote it and isn’t now.
5. Conflict produces confident inaccuracies
Conflict arises when two things in the context disagree, and nothing in the output tells you which one it followed.
6. Contamination introduces potential inaccuracies
With contamination, a mistake makes it into the context, and every step after that treats it as true.
How to find and fix context rot
Pick the workflow you run most often, like the brief skill from our example, and run it once. Then check what it read.
In Claude chat and Cowork, each step Claude takes shows up as a collapsed line in the conversation, showing what was accessed.
Next, identify where that context is called upon. For anything that shouldn’t be there or is out of date, check where Claude pulled it from.
As you make the edits, change one thing at a time, rerun the job, and compare it against the run before. If you change several at once, you’ll see whether the output improved but not which type of context was steering it.
To get the best outputs, we all need to be context engineers. Make context engineering part of your routine to continue to get the most from your favorite AI tool.