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Long prompts do not make AI smarter. In medical testing, they can make it dangerous.
I tested how different prompt styles affect AI accuracy in classifying genetic variants. I used 27 tests with cleared caches to ensure data quality.
Here is what I found:
The Problem with Verbose Prompts Verbose prompts give the AI a detailed "expert role" and list many rules. This sounds good, but it creates a bias.
In one test, the AI saw a benign variant. Because the prompt focused heavily on disease criteria, the AI ignored common population data. It forced a "pathogenic" conclusion where it should have seen "benign."
The Results:
- Verbose Style: 48.1% accuracy.
- Concise Style: 81.5% accuracy.
- Structured Style: 74.1% accuracy.
Concise prompts win on quality. They do not force a specific bias. They allow the AI to evaluate all evidence fairly.
The Thinking Token Tax When using models with reasoning capabilities, longer prompts do not always lead to deeper thought.
- A medium task uses significantly more "thinking tokens" than a simple task.
- A complex task uses only slightly more thinking tokens than a medium task, even if the prompt is 5 times longer.
The AI reaches its thinking limit early. Adding more words to your prompt only increases your cost. It does not increase the intelligence of the answer.
How to build better medical prompts:
- Do not lead with pathogenesis. Never list all disease criteria at the start. This biases the AI toward finding disease.
- Use Concise prompts for accuracy. Short and precise instructions work best.
- Use Structured prompts for speed. JSON-like formats are stable for large batches.
- Use "Step-by-step evaluation" in your output format. Do not rely on prompt length to trigger deep thinking.
- Test with n=3. Never trust a single test result. Cache hits can ruin your data.
Stop writing long prompts. Start writing smart ones.
Optional learning community: https://t.me/GyaanSetuAi