Title: Prompt debugging: when AI doesn't understand what you want

🔍 Why AI "doesn't understand"

Before correcting, let's understand why things go wrong. LLMs don't actually "understand" your instructions — they predict the most likely next sequence. When the result is bad, it is almost always due to one of these causes:

The 7 main causes of bad responses

🩺 The 5-step diagnostic method

Step 1: Identify the type of problem

Before modifying your prompt, classify the problem using this grid: a response that is too vague or generic indicates a context problem; an off-topic response points to a framing problem; a factual inaccuracy signals a hallucination; poor formatting reveals a format problem; an unsuitable length indicates a constraints problem; a close but imperfect result shows a precision problem; and an incoherent response exposes contradictory instructions.

Step 2: Read your prompt like a stranger

Read your prompt by putting yourself in the shoes of someone who knows absolutely nothing about your context. Every ambiguous term, every implicit assumption is a potential source of error. For example, a prompt like "Make me a summary of the report" immediately raises questions: which report exactly? What length is expected? For what audience? What level of detail? And which sections should be highlighted?

Step 3: Isolate the problematic variable

If your prompt is long, test it piece by piece by removing sections one by one to identify the one causing the problem. The method consists of separating tasks: first isolate the analysis by asking only for strengths, weaknesses, and key metrics. In a second step, use this result to ask for concrete improvements with an estimated budget. If the first step works but not the second, the problem lies in the request for improvements, not in the initial analysis.

Step 4: Apply the appropriate fix

Depending on the type of problem identified, apply the corresponding correction (see following sections).

Step 5: Document and capitalize

Note what worked and what didn't. Build your "debugging log" in the form of a text file where you record for each session the date, the topic, the different versions of your prompt, the score assigned to each result, the diagnosis made, and the key changes made — this is how you will become an expert.

🔧 Rephrasing techniques

Technique 1: Progressive specification

Start with a simple prompt and add precision at each iteration in four steps. The first version, too vague (e.g., "Write an article about cloud computing"), will give a generic result. The second version adds the target context (e.g., for non-technical French SME executives), which refines the result but makes it still too theoretical. The third version specifies the structure: 800-word length, focus on concrete savings, inclusion of 3 quantified case studies. Finally, the fourth version finalizes the format by detailing each expected section (title with a number, intro on the problem, three sections with real before/after cases, checklist in conclusion) and the tone (professional but accessible, without jargon).

Technique 2: Inversion (asking for what you DO NOT want)

Sometimes, saying what you don't want is more effective than saying what you want. Rather than simply asking for a professional email—which often yields a cliché and overly formal result—explicitly list the phrases to ban (like "I am taking the liberty of contacting you" or "Please feel free to get back to me"), impose a maximum length limit, and define the desired tone in a positive way (direct, human, like a message between colleagues).

Technique 3: The negative example

Show the model a bad example and ask it to do the opposite. Present a problematic text (for example, a passive-aggressive, vague follow-up email full of clichés), clearly identify its flaws, then ask for a better version respecting specific criteria: provide new useful information, create urgency naturally, respect a line limit, and include a clear call to action.

Technique 4: The "meta" prompt

Ask the AI to help you write a better prompt by providing it with three elements: the desired result, your current prompt, and an example of the mediocre response you are getting. Add a description of the ideal result, then ask the model to rewrite your prompt, explaining what it changed and why.

Technique 5: Chunking (Prompt Chaining)

If a single prompt yields mediocre results, break the task down into several successive steps. Instead of asking in a single block to analyze data, identify trends, propose actions, and write a report, create a chain of four prompts: the first lists key observations with figures, the second identifies the main trends based on those observations, the third proposes concrete actions with estimated impact, and the last synthesizes everything into a structured report.

OpenClaw automates this chaining process, making prompt debugging much easier because you can identify exactly which step is causing problems.

🎯 Solving specific problems

Problem: Responses that are too generic

The diagnosis is clear: it lacks context and specificity. To fix this, enrich your prompt with all relevant information about your situation (company type, tenure, team size, budget) and specify the expected output format. For example, go from a simple "Give me marketing advice" to a request detailing the exact context of a French B2B SaaS startup, asking for 5 actions ranked by impact/effort, with the what, the how, and the target KPI for each action.

To go further on this topic, check out our guide Le guide ultime du prompt engineering en 2025 and our article on Chain-of-Thought, Few-Shot, Tree-of-Thought : les techniques qui marchent.

Problem: Hallucinations (invented facts)

When the model invents facts, apply these fixes: explicitly allow it to say "I don't know" rather than making things up; ask it to categorize each statement (verified fact, estimate, or assumption); limit the scope by requiring it to rely solely on the information you provide; or use cross-verification by testing the same prompt on OpenRouter with multiple models — if the answers diverge on a fact, it is likely invented.

Problem: Incorrect output format

The problem stems from insufficient or ambiguous formatting instructions. Don't just say "Present the results in a table." Describe the exact template expected: precisely name each column with its content (for example: Criterion, Score out of 10, One-sentence comment, Priority), specify the sort order, add specific elements like a final average row, and define a visual system for priorities using emojis.

Problem: Inappropriate tone

The model doesn't grasp the desired register because tone is rarely well conveyed by adjectives alone. The effective technique is to provide a sample of your actual style — a paragraph you have written that exactly represents the voice you want — and then ask the model to write in that same style, using this example as a reference.

Problem: Response that ignores constraints

When too many constraints are buried in the text, the model forgets them. The solution is to visually structure your prompt by clearly separating mandatory constraints (length, language, audience, tone, jargon rules) from the required content (number of examples, specific tables, conclusion elements), using distinct bulleted lists and section headings.

📊 Quick diagnostic matrix

🔄 The iterative debugging workflow

The complete process followed by pros works in a loop: first send the initial prompt, then evaluate the response on a scale of 0 to 10. If the score is 8 or higher, the job is done — save the prompt. Otherwise, diagnose the type of problem, formulate a hypothesis about the probable cause, apply the appropriate correction technique, and re-test with the same modified prompt. Set a maximum of 5 iterations for yourself: if after 5 attempts the result is still unsatisfactory, completely change your approach, break down the task, or test another model via OpenRouter.

🛠️ Debugging tools

Testing on multiple models

Use OpenRouter to submit the same prompt to different models. If Claude gives a good answer but GPT-4 doesn't (or vice versa), the problem lies with the prompt, not the model.

Debugging log

Keep a simple text file where you record for each debugging session: the date and topic, the content of each tested prompt version, the score obtained (out of 10) and the observed flaws, the diagnosis made, and the key changes that led to the final approved version.

💡 Expert tips

1. The "mirror" prompt

Ask the AI to rephrase your request in its own words before answering it, and to wait for your confirmation before starting. This immediately reveals misunderstandings.

2. Integrated scoring

Ask the AI to self-evaluate its response at the end of generation on several criteria (relevance, completeness, clarity), with a score out of 10 for each. If a score is below 7, ask it to explain what is missing and propose an improved version.

3. The quality control prompt

Use a second prompt to evaluate the output of the first. The first prompt produces the content (for example, a prospection email), then a second evaluation prompt scores this content based on a predefined list of criteria and identifies the 3 priority improvements.

4. Temperature as a debugging tool

If the responses are too random or inaccurate, lower the temperature between 0.1 and 0.3. If they are too generic and predictable, raise it between 0.7 and 0.9. For most tasks, a good balance is between 0.4 and 0.6.

🚀 Automating debugging with OpenClaw

OpenClaw allows you to create automated debugging workflows:

1. Main prompt → generates the response
2. QA prompt → evaluates the response according to your criteria
3. Conditional loop → if score < threshold, rephrases and starts over
4. Logging → each iteration is recorded for analysis

The source code of OpenClaw is available on GitHub to customize your debugging workflows.

The Essentials

• Prompt debugging is a methodical skill, not an innate talent
• Always classify the problem before fixing it (context, framing, hallucination, format, constraints, precision, inconsistency)
• Progressive specification (adding precision step by step) solves the majority of problems
• Breaking it down into a prompt chain is the solution for complex tasks
• Documenting each debugging session accelerates learning

Recommended Tools

• Claude — Best model for complex instructions and reasoning
• OpenRouter — Test the same prompt across multiple models to diagnose
• OpenClaw — Automate debugging workflows with quality loops
• Hostinger — Reliable hosting to deploy your AI projects

Common Mistakes

• Changing the entire prompt at once — Modify one variable at a time to identify what works
• Blaming the model — In 90% of cases, the problem comes from the prompt, not the AI
• Not documenting — Without a debugging log, you keep making the same mistakes
• Adding constraints without structuring — A clear bulleted list is better than a dense paragraph
• Stopping after one iteration — The first fix is rarely the right one, plan for 3 to 5 iterations

FAQ

How many iterations does it take on average to debug a prompt?
Between 2 and 5 iterations for a medium-complexity prompt. Beyond 5, change your approach or break down the task.

Does prompt debugging work on all models?
Yes, but the causes vary. Claude tends to be overly cautious, GPT-4 can ignore formatting constraints, and open-source models are less performant on complex tasks.

Should you always document your debugging sessions?
For reusable prompts, yes. For a one-off prompt, a few notes are enough. The important thing is to note what worked so you don't waste time in the future.

Can temperature really help with debugging?
Yes, it's an underused lever. A temperature that is too low makes responses predictable but generic; too high, and they become creative but inaccurate. Adjusting this parameter solves certain tone issues without touching the prompt.

Prompt debugging is not a sign of failure — it's a fundamental skill. The best prompt engineers aren't those who write the perfect prompt on the first try. They are the ones who quickly identify problems and know exactly how to fix them.

With the right methodology and the right tools, you will turn every "bad response" into an opportunity for improvement. And gradually, you will develop an intuition that will help you write better prompts from the start.