# Prompt optimization

A technique for improving prompts used in large language model programs against a performance metric.

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## Optimizing agent system prompts with Amazon Bedrock AgentCore

DevFeed: [Optimizing agent system prompts with Amazon Bedrock AgentCore](<https://devfeed.tech/articles/optimizing-agent-system-prompts-with-amazon-bedrock-agentcore-31522.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/optimizing-agent-system-prompts-with-amazon-bedrock-agentcore/>)

Author: Han Ding

Published: 2026-09-16T15:47:39Z

Content type: article

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Amazon Bedrock AgentCore](<https://devfeed.tech/topics/amazon-bedrock-agentcore.md>), [Prompt optimization](<https://devfeed.tech/topics/prompt-optimization.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Traces](<https://devfeed.tech/topics/traces.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [A/B Testing](<https://devfeed.tech/topics/a-b-testing.md>), [Automated prompt engineering](<https://devfeed.tech/topics/automated-prompt-engineering.md>)

Tags: [a-b-testing](<https://devfeed.tech/tags/a-b-testing.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [amazon-bedrock-agentcore](<https://devfeed.tech/tags/amazon-bedrock-agentcore.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [intermediate-200](<https://devfeed.tech/tags/intermediate-200.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [prompt-optimization](<https://devfeed.tech/tags/prompt-optimization.md>), [system-prompts](<https://devfeed.tech/tags/system-prompts.md>), [traces](<https://devfeed.tech/tags/traces.md>)

### AI overview

This technical companion explains how Amazon Bedrock AgentCore's system prompt optimizer uses production agent traces and reward signals to propose revised system prompts. It describes the reflector engine, recommendation explanations, offline batch evaluation, online A/B testing, and promotion workflow, and reports benchmark results for Single Agent Reflector and the experimental open source Sub-Agent Reflector.

### Source excerpt

AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This technical companion to the launch post explains how the system prompt optimizer's reflector engine works and shares benchmark results for the Single Agent and Sub-Agent Reflectors.

## PROOF-Gen: From Optimized Data to Better Distillation

DevFeed: [PROOF-Gen: From Optimized Data to Better Distillation](<https://devfeed.tech/articles/proof-gen-from-optimized-data-to-better-distillation-6731.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/proof-gen-optimized-distillation>)

Published: 2026-08-26T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Prompt optimization](<https://devfeed.tech/topics/prompt-optimization.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [gemma4](<https://devfeed.tech/topics/gemma4.md>), [Programming](<https://devfeed.tech/topics/programming.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [generate](<https://devfeed.tech/tags/generate.md>), [models](<https://devfeed.tech/tags/models.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [prompt-optimization](<https://devfeed.tech/tags/prompt-optimization.md>)

### AI overview

PROOF-Gen improves tool-calling model distillation by using per-scenario prompt optimization to recover successful trajectories from failed teacher attempts. The method strips corrective guidance before training, producing clean demonstrations and improving benchmark, deployed-pipeline, and on-device model performance.

### Source excerpt

Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher's passing trajectories, discard the rest) and each cycle leaves behind the same hard scenarios because failures supply no signal. On τ 2-bench, 57% of teacher trials fail, two-thirds of them near-misses (most tool calls correct, undone...