Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
📰 ArXiv cs.AI
Learn to optimize system prompts using Bayesian optimization with dynamic representations, improving AI system behavior with limited feedback
Action Steps
- Define the system prompt optimization problem using Bayesian optimization framework
- Implement ReElicit, a Bayesian optimization frame for dynamic representations
- Use aggregate feedback to update the prompt representations and optimize system behavior
- Evaluate the performance of the optimized system prompts using discrete, variable-length text metrics
- Refine the optimization process by incorporating additional feedback mechanisms, such as per-example labels or critiques
Who Needs to Know This
AI engineers and researchers can benefit from this technique to improve system prompt tuning, while product managers can utilize the optimized prompts to enhance user experience
Key Insight
💡 Bayesian optimization with dynamic representations can effectively tune system prompts with limited feedback
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Optimize system prompts with Bayesian optimization & dynamic representations!
Key Takeaways
Learn to optimize system prompts using Bayesian optimization with dynamic representations, improving AI system behavior with limited feedback
Full Article
Title: Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
Abstract:
arXiv:2605.19093v1 Announce Type: new Abstract: System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are difficult to tune when feedback is available only as aggregate metrics rather than per-example labels, failures, or critiques. We study this aggregate feedback setting as sample-constrained black-box optimization over discrete, variable-length text. We introduce ReElicit, a Bayesian optimization frame
Abstract:
arXiv:2605.19093v1 Announce Type: new Abstract: System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are difficult to tune when feedback is available only as aggregate metrics rather than per-example labels, failures, or critiques. We study this aggregate feedback setting as sample-constrained black-box optimization over discrete, variable-length text. We introduce ReElicit, a Bayesian optimization frame
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