In software development, “technical debt” describes expedient design or implementation choices that make future changes harder or more expensive. The debt accrues interest as new work builds on those choices.
In 2026, the AI equivalent became difficult to ignore. It affects programming as well as many other forms of knowledge work. In June, data engineer and data strategist Drew Breunig popularized the term prompt debt. The phrase had appeared occasionally before, but it gained traction after his essay. Breunig examined production applications in which one prompt is supposed to control an entire system. The problem reaches further. Prompt debt exists in prompts, skills, files such as CLAUDE.md, and even loops in which AI generates its own instructions.
On July 21, Simon Willison published a conversation with Anthropic engineers Thariq Shihipar and Cat Wu. They described a striking case: the Claude Code team had cut the system prompt for Fable 5 and Opus 4.8 by 80%. OpenAI likewise recommends leaner prompts with fewer repetitions and examples for GPT-5.6. Shortly before that, Every published “The Case Against Skills”. Its central figure is Mike Taylor, co-author of the textbook Prompt Engineering for Generative AI. His argument is that many prebuilt instruction packages fight capabilities the model already has.
So what is prompt debt?
For years, the standard rule was simple: if an AI fails to do something, or does something it should not do, add an instruction that corrects the error. The longer you use the same prompt, the more corrective instructions accumulate. Sooner or later, they contradict each other. Eventually, the prompt becomes so extensive that even its author can no longer understand it.
My assessment: anyone who has reused and extended AI instructions for long enough has probably accumulated some prompt debt. Even someone who has spent more than a year writing short, understandable prompts and teaching others to do the same can accumulate it elsewhere. I found mine in skills I had built together with Claude and in loops I use with AI agents. There are less obvious hiding places too. I will get to them.
The key points
- The Claude Code team cut the system prompt for Fable 5 and Opus 4.8 by 80%, while retaining the detailed version for older models.
- The best-performing model in the HANDBOOK.md benchmark passed only 36.2% of the tasks under strict scoring when agents had to follow company policies spanning 20 to 124 pages.
- In SWE-Skills-Bench, 39 of 49 public software skills failed to improve the success rate. Some increased token use by as much as 451% without improving the outcome.
- Remove instructions that merely restate trained capabilities and turn rules that are right 90% of the time into conditions. Keep safety boundaries, access restrictions, personal context, and product requirements, then test the old and shortened versions on the same representative tasks. The value of an instruction should be tested, not assumed.
An 80% cut: what changed inside Claude Code
The starting point is the conversation Simon Willison published on July 21. Shihipar explained that the Claude Code team had reduced the system prompt for Fable 5 and Opus 4.8 by 80%. The detailed version remained in place for older models. These were model-specific instructions, not a universal recipe.
Claude Code is Anthropic’s coding agent. Its system prompt governs tool use, working methods, output, and other behavior. Every line had entered the prompt for a concrete reason at some point. The revision suggests, without providing an independently audited performance comparison, that many older instructions were no longer useful for those models.
Willison’s transcript identifies four changes from Claude Code that I also use as practical checks outside software development. The conversation does not test their effectiveness in general knowledge work.
- The team removed examples when they constrained the model more than necessary. Shihipar said the models became more creative without those examples.
- Lists of prohibitions gave way to context. Instead of more instructions in the form “Do not do X,” the prompt supplied information about the required outcome and the boundaries that genuinely mattered.
- The team checked absolute rules for exceptions. Some instructions were correct in 90% of cases and obstructive in the remaining 10%. Those rules became conditional.
- The team maintained the system prompt per model. The shortened version applied to Fable 5 and Opus 4.8, not automatically to smaller or older models.
The underlying pattern is clear. A criterion leaves room for judgment; an absolute prohibition can handle only the situations its authors anticipated.
In June, I analyzed the leaked system prompt of the Claude Fable 5 chatbot: 17,000 words, dozens of sections, repetitions, and outdated passages. That was a different document: Anthropic products use separate system prompts, and the Claude Code team was revising the one for its coding agent. My earlier analysis showed how long and contradictory such a document can become internally.
You can apply one rule from this section immediately. Open your standing instructions, whether that is a user prompt, project description, or favorite work prompt, and search for prohibitions such as these:
- “Never use lists.”
- “Do not write at length.”
- “Do not ask unnecessary questions.”
Rewrite each prohibition as a criterion.
Weak: “Never write at length.”
Better: “Match the length of the answer to the question. A few paragraphs are enough for an everyday question; use as much space as necessary for a professional analysis.”
Why is that better? The criterion works across different situations without requiring you to regulate each one in advance. The Claude Code team described a similar change in its system prompt. The next question is why such a change became necessary and where the excess in your prompts comes from.
Five causes of prompt debt
An 80% reduction raises an uncomfortable question. If a team building AI systems could remove four-fifths of a model-specific prompt, what might be hiding in prompts, skills, and CLAUDE.md files that nobody has ever measured? The answer starts with how the debt accumulates.
Breunig’s definition rests on a distinction worth remembering. Natural-language prompts are excellent for one-off requests and conversational work. A human sees every response and can correct it immediately. They are a poor control mechanism for a system expected to perform the same task, unsupervised, day after day. Breunig observed this in production applications where a prompt had effectively replaced the technical specification. Katie Parrott of Every extended his logic to ordinary users and supplied the definition I use here: prompt debt consists of instructions that have outlived the model for which they were written.
Cause 1: Models improve
From the first versions of ChatGPT through mid-2025, models behaved inconsistently. Prompts therefore needed many instructions and tricks. Taylor recalls using “hacks and magic words” to coax useful code from GPT-3. Those methods were already unnecessary with GPT-4. Greater capability still did not remove every reliability problem. Users continued correcting errors with instructions, constraints, and examples of “what good and bad look like.”
In my own use, older models often ignored explicit instructions, so repeating a central rule two, three, or four times felt normal. The habit survived. In the snapshot of the Fable chatbot system prompt that Breunig examined in June, he counted copyright rules repeated in as many as six places across six different sections.
Cause 2: Correcting the prompt after every failure
The usual response is to add an instruction that prevents the same error next time. The logic makes sense. The problem lies in the maintenance pattern: users readily add new instructions, then forget to check whether those instructions remain necessary after a model upgrade.
Cause 3: Psychology
I still see the same reaction in practice: the AI does the work, but the human remains responsible for it, so users explain every step in minute detail, provide examples of good work, and add prohibitions and constraints. In some prompts, AUTHORS QUITE NATURALLY SWITCH TO ALL CAPS because they hope the AI will work better that way.
All caps does not reliably improve a model’s performance.
This cause cannot be solved by prompt technique alone. You have to change your attitude toward AI. The decisive checks of the result still remain, of course. But if you prescribe and monitor every individual step, it is honestly easier to do the work yourself.
Cause 4: Prompt debt is contagious
You see a prompt with thousands of likes online or download a skill with thousands of stars. Have you tested how it works? Which model generation was it written for? Is its author credible?
Cause 5: Instructions outlive the model
This is the most treacherous form of debt and, according to Breunig, its central symptom. Many corrective instructions are tailored to the model behavior that originally made them necessary. When the model changes, those instructions remain and may fail in unexpected ways.
Datadog observed that GPT-4o remained the most widely used model in its measured traffic as of spring 2026, despite being a model from 2024. Breunig argues that one source of migration friction is accumulated prompt logic tuned to older models. At that point, users no longer control the prompt. The prompt controls its users and binds them to an aging model.
Why 2026 became the tipping point
Why is the debt becoming so visible now? Prompts have been growing for years, and until recently few people cared. The answer seems counterintuitive. The weakness of new models did not turn prompt debt into a problem. Their strength did.
The shift is especially visible in models and guidance designed for agentic work: longer stretches of independent execution without a human correcting every step. That requires more precise instruction following. Suddenly, the multi-layered prompts that older models skimmed can execute far more faithfully. Everything written into them over the years starts to matter at once.
The clearest evidence of this change appears in the current GPT-5.6 guidance. OpenAI recommends outcome-oriented prompts containing the goal, relevant context, boundaries, required evidence, success criteria, and output format. Requests such as “think harder” or “generate multiple candidates” are unnecessary when the selected model and reasoning mode already control that work.
The guide also recommends leaner system prompts. Remove repeated instructions and examples, simplify tool descriptions, and state each instruction once. Examples and style rules should remain when they express a product requirement or close a measured gap. Brevity is not the objective. Test performance is.
A tempting conclusion follows: write rules more carefully, remove contradictions and unnecessary words, and everything will work. Unfortunately, no. The scale of model obedience matters. New models execute individual instructions precisely, which is what makes old prompts dangerous. But there is still a gulf between executing one line and retaining a hundred-page rulebook across fifty work steps. The new generation has not crossed it.
The next blow to the old approach comes from HANDBOOK.md, a benchmark by the evaluation team at Surge AI. Agents received 65 work tasks in virtual companies across five industries. The assignments came with company policies ranging from 20 to 124 pages. The best model passed 36.2% of the tests under strict scoring; most leading models passed fewer than one quarter. The researchers also documented cases in which agents reported compliance even though their actions violated policy. A large rulebook does not create control. It can create the illusion of control.
As a simplified model, the pattern looks like this. With earlier models, an inflated prompt could behave like ballast because parts of it were followed inconsistently. With more instruction-compliant models, the same prompt can cause problems in three ways:
- The model executes outdated rules literally and eagerly.
- Redundant instructions consume context, tokens, and time.
- Very large rulebooks still remain unreliable, including false claims of compliance.
In a sample of OpenAI’s internal coding-agent evaluation runs, configurations with leaner system prompts improved evaluation scores by roughly 10% to 15%. Total token use fell by 41% to 66%, while costs fell by 33% to 67%. OpenAI calls these ranges directional and recommends validating changes on representative tasks from your own application.
You can apply the practical consequence of this section in five minutes. Open your preferred work prompt and search for intensifiers such as:
- “be maximally thorough”
- “check every possible variation”
- “verify repeatedly”
- “achieve complete certainty”
Delete them. Then run typical tasks and check whether the answer quality remains stable. If you work through an API or with an agent, you may also reduce token use and API cost. Sometimes the first useful experiment is simply to delete those intensifiers and compare the result.
One inconsistency remains. If rules, examples, and details should disappear from prompts, what belongs in them? A model may produce something from “Do it well,” but that is not a useful specification. What remains after the cut is what matters.
How to prompt current models: supply context, not generic rules
One formula is enough for this section: do not burden the model with what it already knows. Give it what only you know. Modern models are trained on enormous bodies of text. Repeating generic craft advice makes little sense. What the model cannot infer from general training is your specific situation.
In my analysis of the Fable 5 system prompt, I called this missing information the “gaps.” What do you need the result for? Who will read it? Which decision should it support? What does “good” mean to you? Neither the system prompt nor the model itself was tailored to your situation. You have to supply that missing context.
This gives you a simple test for every line in a prompt. For capable current models, generic instructions about how to write an article are often redundant. Specifying length, structure, and editorial requirements still makes sense. Those constraints are not in any book because they come from you.
Two cases deserve special attention. The first is a narrow field with very little published material worldwide: an internal company process, a regional peculiarity, a rare method, or a tightly defined profession. The second is anything that happened after the model’s knowledge cutoff. I begin such a session with this sentence:
“We are working on X. First, collect current information on the subject. It is now August 2026.”
A browsing-enabled model can then search instead of relying only on older training data.
The outcome-first formula in OpenAI’s guide can be translated into four components: outcome, acceptance criteria, constraints, and concrete context. In my first article about prompting, I presented a detailed laptop-selection prompt with five blocks: task, output format, constraints, self-check items, and context. The same task, written according to the 2026 formula, looks like this:
“Help me choose a laptop. I carry it every day, so it must weigh no more than 1.8 kg. I work in a browser with 20+ tabs and in Figma, and I play Civilization in the evening. My budget is €800–1,200. The task is complete when you have found 3–5 current models available at that price, each with verified links to stores. If you need more information to make the selection, ask before you start searching.”
Two blocks have disappeared compared with the old version. The output format is absent because the model can choose a table itself. If you have a fixed requirement, include it as a one-line constraint. The self-check has also disappeared because it is already embedded in the acceptance criteria: “current models,” “available,” and “verified links.” The prompt is much shorter. The context, however, was expanded rather than shortened. My rule is simple: cut rules, not information.
I ran a quick test of the prompt in a standard ChatGPT chat and got this result:
The original German test: ChatGPT asks for two missing details before researching models and store links. Screenshot from the author’s August 14, 2026 test.
My prompt, translated:
“Help me choose a laptop. I carry it with me every day, so it may weigh no more than 1.8 kg. I work in a browser with more than 20 tabs and in Figma; in the evening I play Civilization. Budget: €800–1,200. The recommendation is complete when you have found three to five current models available in that price range, each with verified shop links. If you still need information for the selection, ask before making a recommendation.”
ChatGPT’s response, translated:
One critical detail is still missing for a reliable recommendation: Where will you buy it? Germany, Austria, or another EU country? Prices and availability differ considerably, and you explicitly want verified shop links.
I also have one preference question: By “Civilization,” do you mainly mean Civilization VII? If so, I would tend to recommend models with a stronger graphics card. Civilization VII recommends roughly an RTX 2060/RX 6600-class GPU for Windows with 16 GB RAM; a laptop RTX 4050 can even manage Full HD at high settings at more than 60 fps, according to benchmarks.
Just tell me country + Civ VI or VII. I will then find three to five currently available models for €800–1,200, no more than 1.8 kg, suitable for 20+ tabs/Figma and gaming, and check the specific shop listings.
This is an English translation of the visible exchange, not the result of a second ChatGPT run. It is a single demonstration, not a benchmark of how often ChatGPT asks clarifying questions.
In this test, ChatGPT asked the two missing questions itself. In tools that support browsing or clarification, models can gather some missing knowledge rather than forcing you to anticipate every gap. The data analysis in One Prompt, Twelve Searches shows how far a single request can fan out into subqueries.
So far, that behavior mainly helps with smaller examples. For a substantial task, especially one assigned to an AI agent, the work starts before the prompt. Identify the information gaps and prioritize the questions that matter most. I previously described this preparation through the image of “map and territory,” following guidance from Thariq Shihipar. Preparation does not mean inflating the instructions. Its output is a short specification with good references, not a rulebook.
HANDBOOK.md documents this risk for 65 agentic workplace tasks governed by 20- to 124-page policies; it does not test articles, presentations, or generic work reports. I use that finding as a practical reason to inspect the deliverable itself rather than accept an agent’s summary as proof of completion. If the output is program code, a presentation, an article, or an outline, inspect that artifact rather than the report about it.
If you cannot assess the artifact directly, load it into a new chat or a new AI agent and request an analysis. Even with the same model, the new chat approaches an unfamiliar result without the context of having created it. A separate review agent is useful for precisely this reason: it does not have the artifact’s creation history in context.
The one-line checklist for this section is: expected result, acceptance criteria, constraints, your context. Review the artifact, not the story told about it.
All of this advice concerns prompts you write now. Most prompt debt is elsewhere. It sits in the archive: Google Docs full of “favorite prompts,” skills you have not opened for months, the model’s memory about you, and loops in which the AI instructs itself.
Three places where prompt debt hides
Visible prompts are the easy part because the text is right in front of you. Debt becomes harder to find when it accumulates where nobody looks. I know three such places. One became a problem for me personally.
Trap 1: Models teach each other bad habits
In my own tests, models sometimes reproduce prompting patterns from 2024 and 2025. In one test with Fable, I received a draft built around an approach that was already outdated.
While working on this article, I wanted a prompt that could remove obsolete elements from old prompts and skills. I gave the task to Fable. This was the result:
“Here is my [skill / CLAUDE.md / user prompt]. First analyze it without rewriting anything. Assign every instruction to one of these categories: (1) invariant: a fact or constraint you cannot discover yourself; (2) duplicate of trained behavior: you do this without instruction; (3) relic: written for an old model or an error that can no longer be reproduced; (4) 90% rule: usually correct but written as ‘always/never’; (5) conflict: contradicts or overlaps another line. Recommend deletion for (2) and (3), conditional wording for (4), and which line should remain for (5). Leave safety and access rules untouched. At the end, show a shortened version and a list of deleted items, each with a one-line explanation. I make the decision about every line.”
Look closely. Several problems appear immediately.
- The instructions conflict. The opening says nothing should be rewritten, while the ending asks for a shortened version.
- Analysis and action are mixed together. The user is supposed to decide on every line, yet Claude supplies a completed short version in the same response.
- The detailed five-part review procedure itself clashes with the newer prompting rules. That level of detail may still be justified, however, because current models do not necessarily know these categories by default.
My conclusion is straightforward. Be careful when asking an AI to write a prompt from scratch. It will supply a usable draft. Remove everything unnecessary from the final version. Later in this article, I explain how to refactor prompts manually and provide a much simpler prompt I use myself.
Trap 2: Skills
A skill is also a prompt, only stored for reuse: an instruction package the model loads for a particular type of task. Skills can therefore accumulate debt too.
Start with skills downloaded from the internet. Taylor’s position in “The Case Against Skills” has now been tested in a narrow setting by SWE-Skills-Bench. The researchers selected 49 public programming skills and ran the same tasks with and without each one. Thirty-nine of the 49 skills produced no improvement. The average gain was 1.2%, while individual skills increased token use by as much as 451% for the same outcome. The March study tested software-engineering tasks with Claude Code and Haiku 4.5. Its authors identify the narrow benchmark context as a limitation. The figures do not automatically transfer to writing, research, or other knowledge work.
The benchmark tested public skills written by strangers for software-engineering tasks. That detail exposes another problem. People often download popular skills that promise better programming or web design, or claim to cure AI slop. A skill’s author wrote it for their own work, not yours. Their definition of “improvement” is not necessarily yours. Taylor’s principle therefore convinces me: every skill must earn its place in your library through measurement, not through a vague sense of usefulness. Select several tasks, run them with and without the skill, and compare the results. If you dislike the result, the skill does not fit your work.
I started using skills with Claude during the winter, first in a chatbot for web search and writing, then in Claude Code. My process worked like this. I discussed a task with Claude and developed a draft workflow. I tested it in several experiments, gave feedback, and asked Claude to turn the stable rules into a skill.
Whenever the model made a mistake or I noticed room for improvement, I described it to Claude and had the skill revised.
At first, this worked well. The skill made the work faster, and the model produced results I liked. Later, the quality slipped. I assumed this was the familiar effect of being satisfied with a piece of work at first and noticing its weaknesses only later. I kept revising the skill in the same way without recognizing the underlying problem.
The release of Opus 4.7 brought the collapse. With the same skill, the model suddenly wrote terrible copy: broken structure, strange language, and completely unreadable headings. In small writing experiments without the skill, the model performed reasonably well.
After another failed article, I told Opus directly in the chat how to revise it. The model unexpectedly refused with the words, “The skill forbids it!” Only then did I open the skill itself, and what I found horrified me. Over several months, the model had filled it with instructions and examples covering what to do and what to avoid. It was a textbook case of prompt debt.
Since then, I have followed one rule: do not merely extend a skill. Refactor it regularly and remove anything obsolete or obstructive. The same applies to the next trap.
Trap 3: Loops in which AI instructs itself
These loops may become a major source of prompt debt because they repeatedly generate and revise their own instructions. I described their mechanics in my article about loop engineering: an AI prompts itself, repeatedly evaluates the result, and tries to improve it through revised prompts. Without deliberate review, those revisions can keep recycling outdated prompting patterns.
Nobody can responsibly predict how quickly this pattern will change with new model generations. Until then, use the same remedy as with skills. Periodically pause the loop, inspect its instructions with the model, and remove anything obsolete or redundant.
How to refactor prompt debt
Technical-debt reduction works best as routine maintenance. The practical principle is simple: reserve a fixed amount of time for paying it down.
The psychology is easy to understand. Suppose you configure your AI well and save two hours every day. If you do nothing, those savings can shrink as old instructions accumulate. Eventually, you have to rebuild the process. Reserving part of the time saved for refactoring protects the process from that drift. You do not have to work on prompts and skills every day. I prefer to do it every two weeks and reserve two hours at a time.
The process starts by collecting all persistent instructions. You then assess every line: is it an invariant the model cannot know, or a judgment that merely asks for vague good behavior? Temporarily remove judgments so you can test whether they add measurable value. Check surviving invariants for exceptions, convert 90% rules into conditions, and remove context the model can derive from the material. Finally, test the old and shortened versions on two or three typical tasks using identical acceptance criteria. If the shortened version performs worse, restore the instruction that proved effective. If it performs equally well or better, keep the shorter version.
Step 1: Take inventory
Collect every persistent instruction in one document: your user prompt, project instructions, skills, CLAUDE.md or its equivalent, and any cross-chat memory the model stores about you. I explain where these elements live in my article about the six areas of personalization. The essential point is enough here: instructions are scattered across different corners of the interface. That is half the problem. You only need to complete this inventory once. After that, everything is available in one place.
Do not try to refactor all of it immediately. Start with tasks that are current and useful but not critical. There is no guarantee that the first attempt will work perfectly. Damaging a critical process would be a poor experiment. Refactoring the prompt for a vacation you will not plan for another year is equally unhelpful.
Step 2: Separate invariants from judgments
Go through the list and assign each line to one of two types. An invariant is a fact, boundary, or binding preference the model cannot infer reliably: your name, your profession, your language conventions, or a requirement to use tables only for comparisons. A judgment asks for something the model already knows how to do: “Write correctly,” “Pay attention to details,” “Answer professionally,” or “Check the logic.”
The rule is simple: keep invariants, and treat judgments as candidates for removal. The comparison test in Step 5 determines whether any of them should return. You do not make a model more attentive by ordering it to pay attention. You merely force it to guess what you meant.
Step 3: Turn “always” and “never” into conditions
Apply the method the Claude Code team calls “90/10” to every line that survives Step 2. If a rule is correct in 90% of cases, do not phrase it as a prohibition. State when it applies. “Always answer briefly” becomes: “Answer everyday questions briefly and professional analyses in detail.” OpenAI’s recommendation points in the same direction. Reserve absolute wording for genuine prohibitions, meaning actions that must never occur under any circumstances.
In my own long prompts, this test tends to leave two or three absolute lines, not twenty.
Step 4: Remove what the context already implies
Some prompt debt simply repeats information the model can infer. If you ask a model to work with code, you do not need to add “Answer as if speaking to a developer.” The model selects that register by default. If you attach editorial guidelines, you do not need to restate each point in the prompt.
The reverse case also exists. Suppose you find a preprint of an important mathematics paper, download it, give it to the AI, and ask what the authors did. The model will address you as a mathematician by default. If you are not one, add this line to the prompt:
“Explain it to a layperson.”
Step 5: Measure
Remember OpenAI’s warning that its figures are directional and should be tested on your own tasks? Apply the same skepticism to this article. Do not take my word, or Anthropic’s, on faith. Choose two or three typical tasks. Run each one once with the old instruction set and once with the shortened version, then compare the outcomes.
Does the shorter version perform worse anywhere? Good. You have found an instruction that actually works. Restore it without guilt. Revision is not an ideology of total minimalism. It distinguishes effective lines from ballast.
When not to shorten prompts too aggressively
Genuine invariants, personal facts, and every safety or access restriction should remain. These include rules such as “Do not send emails without confirmation,” “Do not touch the production environment,” and “Do not use payment data.” They are not debt. They are seat belts, and their value does not depend on how often you need them.
The same applies to internal expertise, regional specifics, and binding product requirements. If the model cannot know the information on its own, length is no argument for deleting it.
Examples and format rules may remain too when a comparison test shows a measurable advantage. Refactoring follows the results of your tasks, not an ideal of the shortest possible prompt.
I promised a revised simplification prompt. It handles the most tedious part of refactoring by sorting lines into categories. It differs from Fable’s version in one central respect: it works in two stages, keeping analysis separate from action. The model does not present a finished shortened version before you have made your decisions. Otherwise, you are merely approving someone else’s selection rather than deciding for yourself.
Stage one:
“Here is my [skill / user prompt / CLAUDE.md]. Analyze it without rewriting anything. Assign every instruction to one category. If two categories apply, give the most important one: 1) Invariant: a fact or constraint you cannot discover yourself; 2) Duplicate of trained behavior: you do this without instruction; 3) Relic: the instruction was written for an old model or an error that no longer occurs; 4) 90% rule: the instruction is usually right but phrased as ‘always’ or ‘never’; 5) Conflict: the instruction contradicts or duplicates another line. For every line, give a one-sentence recommendation: keep, delete, or rewrite as a condition. Mark safety and access restrictions as invariants. I will make the decisions afterward.”
Once you receive the analysis, review the list and make your decisions. This moves quickly because the categories are already in place. Then comes stage two:
“Create the final version based on my decisions: [list]. Add nothing of your own.”
Two simple methods help with ongoing maintenance.
The first is the “clean run” I previously recommended in the Fable 5 article. When a new model appears, disable the user prompt, cross-chat memory, and every skill. Run a few simple standard tasks with those customizations switched off. This gives you a cleaner baseline. Then turn everything back on and compare the quality on the same tasks. If it falls, refactoring is due.
The second method is a prompt I have used for more than a year to improve prompts, skills, and related instruction files:
“Read [prompt / skill / CLAUDE.md]. What is implemented well? What is missing for the work? What restricts you while working? Do any instructions contradict each other? List every suggestion. I will decide what to improve.”
This prompt does not replace refactoring. It lets you examine the task through the model’s “eyes”: what works for it, what does not, and what it would like to change. Do not accept every change automatically. Select only the best suggestions.
Conclusion: Test prompt changes instead of guessing
A modern prompt does not have to be short. It has to contain effective information and discard rules that have outlived their purpose. Keep context, invariants, and safety boundaries. Remove restatements of trained capabilities, repetitions, and unmeasured prohibitions. A comparison on your typical tasks decides whether the shortened version is better.
Frequently asked questions about prompt debt
What is prompt debt?
Prompt debt consists of persistent instructions written for an earlier model, an earlier error, or a different working context. On a new model, they may become redundant, create conflicts, or trigger unnecessary work.
How can I recognize prompt debt?
Typical signals include repeated rules, long lists of prohibitions, absolute words such as “always” and “never,” examples with no measured benefit, and instructions a current model already follows reliably.
Which rules should I never delete?
Keep safety and access boundaries, personal facts, binding product requirements, and context the model cannot discover by itself. These are invariants, not ballast.
Are long prompts always bad?
No. Length alone is not a sufficient criterion. A long prompt may be necessary when it contains genuine context or binding requirements. Contradictions, repetitions, and instructions without measured benefit are the problem.
How do I test a shortened version?
Run two or three typical tasks once with the old version and once with the shortened version. Compare output quality, rule violations, token use, runtime, and cost. If a relevant result deteriorates, restore the effective instruction.