RL Agent that can auto-optimize your LLM prompts
Hey everyone! Along with my team, I've developed a reinforcement learning system that automatically optimizes LLM prompts, complete with a visualization feature to track both prompt structure and learning progress over time. Take a look here: https://nomadic-ml.github.io/nomadic/cookbooks/Nomadic_Promp... Check out our website too:https://www.nomadicml.com/ In terms of how this visualization works: The RL Prompt Optimizer employs a reinforcement learning framework to iteratively improve prompts used for language model evaluations. At each episode, the…
RL Agent that can auto-optimize your LLM prompts launched on November 8, 2024 with 14 votes, #1,069 of 4,990 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
This tool uses reinforcement learning to automatically improve prompts for large language models through iterative optimization. The system evaluates prompt performance across multiple metrics and adjusts prompt structure based on reward signals, helping users develop better-performing prompts. It includes visualization features to monitor both the prompt modifications and the learning progress over time. The tool is designed for developers and researchers working with language models who want to enhance prompt quality systematically.
written from the facts on this page · September 2026
From the sources
The RL Prompt Optimizer employs a reinforcement learning framework to iteratively improve prompts used for language model evaluations. At each episode, the agent selects an action to modify the current prompt based on the state representation, which encodes features of the prompt. The agent receives rewards based on a multi-metric evaluation of the model's responses, encouraging the development of prompts that elicit high-quality answers. weights = { "faithfulness": 0.4, # Context adherence "correctness": 0.3, # Response accuracy "relevance": 0.2, # Query relevance "clarity": 0.1 # Comprehensibility } Performance Summary Metric Value Best Score Achieved 0.7333333333333333 Average…from nomadic-ml.github.io
In the maker’s words, at launch
Hey everyone! Along with my team, I've developed a reinforcement learning system that automatically optimizes LLM prompts, complete with a visualization feature to track both prompt structure and learning progress over time. Take a look here…
Does a similar job
all alternatives →- APAutomatic prompt optimizer for LLMs2023 · promptperfect.jina.ai · ▲126
Prompt Engine – Auto pick LLMs based on your prompts2024 · jigsawstack.com · ▲93Nowadays, a common AI tech stack has hundreds of different prompts running across different LLMs. Three key problems: - Choices, picking from 100s of LLMs the best LLM for that 1 prompt is gonna be challenging, you're probably not picking the most optimized LLM for a prompt you wrote. - Scaling/Upgrading, similar to choices but you want to keep consistency of your output even when models depreciate or configurations change. - Prompt management is scary, if something works, you'll never want to touch it but you should be able to without fear of everything breaking. So we launched Prompt…
- RARelari – Auto Prompt Optimizer as Lightweight Alternative to Finetuning2024 · ▲32
Hi HN, we are the founders of Relari (https://www.relari.ai). We launched our LLM evaluation stack on HN a few months ago (https://news.ycombinator.com/item?id=39641105), which is now used in production by AI teams at companies like Vanta and PwC. We have since expanded to directly optimizing parts of an LLM pipeline using a data-driven approach. In particular, we see a lot of potential in the Auto Prompt Optimization—which could be an attractive alternative to fine-tuning in many cases—to use data to align LLMs for domain-specific tasks. Here’s a demo video:…
Improve LLM Performance by Maximizing Iterative Development2024 · github.com · ▲104I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations, latency, cost), you need to try various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, different agent architecture, and more. There are thousands of possible combinations and you need a process…
Prompt Optimizer2025 · mindpal.space · ▲17Free AI Prompt Optimizer - Improve Your AI Prompts Instantly. Supports ChatGPT, Claude, and all major AI models.
PrompTessor2025 · promptessor.com · ▲125After countless hours of solo coding, designing, and iterating, I’m both excited and nervous to share my first-ever product: PrompTessor — now live. As someone who constantly experiments with AI tools like ChatGPT and Midjourney, I often found myself stuck in a loop of trial-and-error. The issue wasn’t always the AI — it was often the prompt itself. That observation became the starting point for PrompTessor. This tool doesn’t just optimize your prompt — it analyzes it. It gives you: - A prompt score - A breakdown of strengths and weaknesses - A fully optimized version of your prompt - And…
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