Show HN: Improve LLM Performance by Streamlining Iterative Development https://ift.tt/deMBAY8

Show HN: Improve LLM Performance by Streamlining Iterative Development 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, so you need a process that let’s you quickly test and evaluate these different combinations to maximize your LLM performance. I have been working in AI since 2021 - first at FAANG with ML, then with LLM in start-ups since early 2023. I have had the chance to talk with many companies working with AI. Using my learnings, I’m working on an Open Source Framework that structures your application development for rapid iteration so you can easily test different combination of your LLM application components and quickly iterate towards your accuracy goals. You can checkout the project on Github. If you are interested in trying it out, you can get a complete LLM chat application setup locally with a single command. Stars are always appreciated! https://ift.tt/TfXExDr July 3, 2024 at 02:52AM