I like the support for Vector DBs and LLaMa-2. I'm curious as to whether and what influences compelled PromptTools, and how it differs from other tools in this space. For context, we've also released a prompt engineering IDE, ChainForge, which is open-source and has many of the features here, such as querying multiple models at once, prompt templating, evaluating responses with Python/JS code and LLM scorers, plottin…
Great question, chainforge looks interesting! We offer auto-evals as one tool in the toolbox. We also consider structured output validations, semantic similarity to an expected result, and manual feedback gathering. If anything, I've seen that people are more skeptical of LLM auto-eval because of the inherent circularity, rather than over-trusting it. Do you have any suggestions for other evaluation methods we should…
For suggestions, one thing I'm curious about is how we can have out-of-the-box benchmark datasets and do this responsibly. ChainForge supports most OpenAI evals, but from adding this we realized the quality of OpenAI Evals is really _sketchy_... duplicate data, questionable metrics, etc. OpenAI has shown that trusting the community to make benchmarks is perhaps not a good idea; we should instead make it easier for scientists/engineers to upload their benchmarks and make it easier for others to run them. That's one thought, anyway.