Show HN: PromptTools – open-source tools for evaluating LLMs and vector DBs
📰 Hacker News · krawfy
Learn to evaluate LLMs and vector DBs efficiently with PromptTools, a new open-source toolkit
Action Steps
- Install PromptTools from GitHub using 'git clone https://github.com/hegelai/prompttools.git'
- Configure PromptTools to work with your LLM and vector database using 'python configure.py'
- Use PromptTools to evaluate the quality of your LLM's responses for a given use case, such as testing generated JSON or SQL output
- Apply PromptTools to benchmark and compare the performance of different LLMs and vector databases
- Test the semantic similarity of generated answers to expected answers using PromptTools' evaluation metrics
Who Needs to Know This
Engineers and product developers can benefit from PromptTools to streamline the evaluation process of LLMs and vector databases, saving time and improving product quality
Key Insight
💡 PromptTools simplifies the evaluation process of LLMs and vector databases, enabling engineers to test and benchmark models more efficiently
Share This
🚀 Evaluate LLMs and vector DBs faster with PromptTools! 🚀
Key Takeaways
Learn to evaluate LLMs and vector DBs efficiently with PromptTools, a new open-source toolkit
Full Article
Hey HN! We’re Kevin and Steve. We’re building PromptTools ( https://github.com/hegelai/prompttools ): open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. Evaluating prompts, LLMs, and vector databases is a painful, time-consuming but necessary part of the product engineering process. Our tools allow engineers to do this in a lot less time. By “evaluating” we mean checking the quality of a model's response for a given use case, which is a combination of testing and benchmarking. As examples: - For generated JSON, SQL, or Python, you can check that the output is actually JSON, SQL, or executable Python. - For generated emails, you can use another model to assess the quality of the generated email given some requirements, like whether or not the email is written professionally. - For a question-answering chatbot, you can check that the actual answer is semantically similar to an expected answer. At
DeepCamp AI