Relari – Auto Prompt Optimizer as Lightweight Alternative to Finetuning
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:…
Relari – Auto Prompt Optimizer as Lightweight Alternative to Finetuning launched on August 28, 2024 with 32 votes, #844 of 5,471 launches that month and more than 75% of that year's launches.
In plain words
Relari provides an auto prompt optimizer that improves large language models for specific tasks using data-driven methods. Designed for AI teams building LLM applications, it offers an alternative to fine-tuning that requires less overhead. The tool automatically develops and refines prompts across different use cases and models, helping teams adapt to model updates and changing requirements without manual iteration.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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…
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…
Create your own finetuned AI model using Google Sheets2025 · promptrepo.com · ▲137Hello HN, We built Promptrepo to make finetuning accessible to product teams — not just ML engineers. Last week, OpenAI’s CPO shared how they use fine-tuning for everything from customer support to deep research, and called it the future for serious AI teams. Yet most teams I know still rely on prompting, because fine-tuning is too technical, while the people who have the training data (product managers and domain experts) are often non-technical. With Promptrepo, they can now: - Add training examples in Google Sheets - Click a button to train - Deploy and test instantly - Use OpenAI,…
- RARL Agent that can auto-optimize your LLM prompts2024 · nomadic-ml.github.io · ▲14
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…
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…
Argmin AIJul 2026 · argminai.com · ▲32Hey, HN community! We've built Argmin AI after shipping LLM features where the demo worked, then the bill and latency got unpredictable in production. Prompts expanded, context grew, retrieval got noisy, retries appeared, and agent workflows added loops. Argmin AI optimizes LLM-related expenses as a system: 1. prompt and context efficiency 2. model selection and routing 3. RAG inefficiencies and caching opportunities 4. agent workflows (tool calls, retries, loop control) Changes are validated with evals and guardrails (tests, gates, judges), tailored to your quality definition and goals.…
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