Show HN: [OSS] Reduce LLM Hallucination by Streamlining Experimentation https://ift.tt/6fLhmdE

Show HN: [OSS] Reduce LLM Hallucination by Streamlining Experimentation *Problem* Let's assume you are building a simple Q&A Chatbot backed by RAG. To get the best possible answer for a given question, you need to find the right combination of prompt and LLM model. To find the right prompt, you need to identify the correct prompt template (e.g., chain-of-thought, few-shot, react, etc.) with the appropriate RAG context. To get the right RAG context, your Vector DB needs to have the correct data and indexing. In this simple, non-conversational single-agent application, to obtain the best answer for your application, you need to find the right combinations of Model X Prompt Template X Context X RAG Content. There are likely thousands of permutations to try, and it's challenging to determine which combination would yield the best result. Therefore, to find the best possible combination, you need a process that can help you quickly run experiments to evaluate these different combinations against the use-cases your application needs to support, and assist you in objectively comparing each combination against each other. *Solution* We are working on an Open Source project to help development teams streamline this experimentation process. We are doing this by helping you: 1. Build a modular LLM applications that let’s you easily test different configuration of your application 2. Setup an objective accuracy benchmark tailored to your application 3. Scale running lots of experiments quickly 4. Analyze experiment results with analytics tools and tracing 5. Deploy your application and integrate it to the rest of your system using REST API or SDK https://ift.tt/rWS5B7s June 29, 2024 at 10:46PM