# Prompt optimization Published articles for Prompt optimization. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Optimizing agent system prompts with Amazon Bedrock AgentCore DevFeed: [Optimizing agent system prompts with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Han Ding Published: 2026-09-16T15:47:39Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Prompt optimization](), [Prompt Engineering](), [Traces](), [Benchmark](), [A/B Testing](), [Automated prompt engineering]() Tags: [a-b-testing](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [benchmark](), [best-practices](), [evaluation](), [intermediate-200](), [open-source](), [prompt-optimization](), [system-prompts](), [traces]() ### 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]() Original publisher: [Read original article]() Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Fine-tuning](), [Prompt optimization](), [Artificial Intelligence](), [gemma4](), [Programming]() Tags: [agent](), [ai-agents](), [evaluation](), [fine-tuning](), [gemma](), [generate](), [models](), [on-device](), [post-training](), [prompt-optimization]() ### 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... ## Promptimus: Improving already good LLM prompts with zero manual engineering DevFeed: [Promptimus: Improving already good LLM prompts with zero manual engineering]() Original publisher: [Read original article]() Author: Zhengyuan Shen; Yunfei Bai; Sullam Jeoung; Shuai Wang Published: 2026-05-14T13:47:45Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Automated prompt engineering](), [Prompt Engineering](), [Optimization](), [Large Language Model](), [AI Agent](), [Artificial Intelligence](), [multimodal](), [Code]() Tags: [ai](), [ai-agent](), [amazon-bedrock](), [automated-prompt-engineering](), [code-generation](), [conversational-ai](), [engineering](), [generative-ai](), [large-language-models-llms](), [llm](), [multimodal](), [optimization](), [performance](), [prompt-engineering](), [prompt-optimization]() ### AI overview Promptimus is an automated prompt-engineering method for improving already strong prompts without manual engineering. It uses task data, user-defined performance metrics, failure analysis, debugging agents, sanitization, and targeted edit mode to refine prompts while preserving working business logic. The method is model agnostic and supports textual and multimodal LLM tasks, including classification, extraction, generation, summarization, code generation, and tool use. ### Source excerpt By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality. ## AI Engineering Primer DevFeed: [AI Engineering Primer]() Original publisher: [Read original article]() Published: 2024-12-19T00:00:00Z Content type: tutorial Language: en Sources: [Tim Kellogg]() Topics: [AI Engineering](), [Large Language Model](), [LangChain](), [DSPy]() Tags: [advice](), [ai-engineer](), [ai-engineering](), [dspy](), [langchain](), [learning](), [llms](), [prompt-optimization]() ### AI overview A primer on getting up to speed with AI engineering. It discusses the field's rapid evolution and advises readers to be cautious with LangChain tutorials, prompt influencers, and blindly applying traditional machine-learning advice. It mentions alternatives including hand-written code, LlamaIndex, DSPy, Gradio, and vendor SDKs. ### Source excerpt How do you get up to speed with AI engineering? Unfortunately, I don't know of any good consolidated resources, so I'm going to attempt to make one here. My first attempt at this focused more on what an AI engineer is and made only a feeble attempt at providing resources to get started. Let's go!