# Automated prompt engineering

Automated prompt engineering uses automated methods to design, refine, and optimize natural-language prompts for large language models.

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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.

## Promptimus: Improving already good LLM prompts with zero manual engineering

DevFeed: [Promptimus: Improving already good LLM prompts with zero manual engineering](<https://devfeed.tech/articles/promptimus-improving-already-good-llm-prompts-with-zero-manual-engineering-7605.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/promptimus-improving-already-good-llm-prompts-with-zero-manual-engineering>)

Author: Zhengyuan Shen; Yunfei Bai; Sullam Jeoung; Shuai Wang

Published: 2026-05-14T13:47:45Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Automated prompt engineering](<https://devfeed.tech/topics/automated-prompt-engineering.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [automated-prompt-engineering](<https://devfeed.tech/tags/automated-prompt-engineering.md>), [code-generation](<https://devfeed.tech/tags/code-generation.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llm](<https://devfeed.tech/tags/llm.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [prompt-optimization](<https://devfeed.tech/tags/prompt-optimization.md>)

### 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.

## Entropix dynamically adapts LLM execution strategies based on model signals

DevFeed: [Entropix dynamically adapts LLM execution strategies based on model signals](<https://devfeed.tech/articles/why-is-entropix-important-33441.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2024/10/20/entropix-part2>)

Published: 2024-10-20T00:00:00Z

Content type: opinion

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [Automated prompt engineering](<https://devfeed.tech/topics/automated-prompt-engineering.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [JIT](<https://devfeed.tech/topics/jit.md>)

Tags: [hallucinations](<https://devfeed.tech/tags/hallucinations.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-techniques](<https://devfeed.tech/tags/llm-techniques.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>)

### AI overview

The article explains entropix as a proposed framework for LLM execution that uses signals from a model's internal state to switch dynamically among techniques such as chain-of-thought prompting and parameter adjustments. It presents this as an approach to automate some prompt-engineering decisions and notes that the current implementation relies on crude heuristics, with reinforcement learning being explored as a replacement.

### Source excerpt

The buzz is there! The cooks are cooking! But what is entropix? I wrote a fluff piece here explaining it, but the most comprehensive and complete description of entropix is here.