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cloud.google.com·7h ago
AI Research
arXiv CS.AI·10d ago·5 min read
Scientific Agents: Evaluating Profession-Specific System Prompts on Scientific Tasks
TL;DR
Evaluating the effectiveness of profession-specific prompts in AI models revealed no significant accuracy improvements while increasing costs and token usage. The findings suggest that longer prompts may offer some advantages, but overall, defaulting to full profiles is not beneficial.
✦ Why It Matters
Consider using shorter, generic prompts to reduce costs without sacrificing accuracy in AI applications.
Key Takeaways
- Evaluate the cost-effectiveness of prompts in AI models.
- Test shorter prompts to improve response efficiency.
- Consider selective retrieval of profile sections for better performance.
- Monitor API performance to mitigate drop issues.
Full Summary
This study assessed the impact of detailed, profession-specific prompts on the performance of AI models in scientific tasks. Using the Gemini 3.8 Flash model, researchers evaluated 503 profiles against a minimal baseline and other controls across nine science benchmarks, totaling 4,531 questions.
Results showed an average accuracy difference of -0.6 percentage points, with no benchmarks indicating a clear improvement from using specific profiles. Additionally, matched profiles generated 1.5-2.3 times more output tokens and incurred 2.2-4.5 times higher costs per successful call.
In bioinformatics tasks, the baseline performed better, with a 10 percentage point advantage. Interestingly, longer prompts did yield better performance in some cases, suggesting that prompt length or formatting may be more critical than domain expertise.
Overall, the findings indicate that using full profession profiles by default does not enhance accuracy and significantly increases costs.