Clean Up a Research Prompt
Research prompts often demand thoroughness three times and then quietly also ask for brevity. Here is how to clean and de-conflict them.
Overview
Research prompts accumulate two problems at once: repeated demands for rigour ('be thorough, be comprehensive, do thorough research') and a hidden contradiction where 'be detailed' sits next to 'keep it short'. Both confuse the model. This resource shows how to clean a research prompt — collapsing the repeated rigour instructions and surfacing the length conflict so you can decide what you actually want.
Workflow
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Paste the research prompt
Load the prompt with its repeated rigour and source instructions.
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Run Balanced Clean
Repeated 'thorough / comprehensive' and 'cite sources' instructions collapse to one each.
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Resolve the length conflict
The report flags 'comprehensive' against 'keep it short'. Decide which depth you actually want and remove the other.
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Run the cleaned prompt
A prompt with one clear depth instruction produces consistent research depth instead of variable output.
Why This Works
- Restating 'be thorough' three times competes with the source and citation instructions rather than reinforcing rigour
- The short-vs-comprehensive conflict is the single most common reason research output depth varies run to run
- Resolving the conflict once, before running, is cheaper than re-running until the depth happens to land right
Best for
- Research and analysis prompts with repeated 'be thorough' style instructions
- Prompts producing inconsistent depth because length instructions conflict
- Prompts where source and citation requirements are restated multiple times
Not for
- Adding research scope or criteria — the cleaner removes redundancy, it does not define methodology
- Prompts where each rigour instruction targets a genuinely different dimension
Use cases
- Cleaning a comparison or analysis prompt that demands rigour in several restated lines
- Surfacing the short-vs-detailed conflict common in research prompts
- Trimming a research prompt before running it against a large source set