| Hey HN - Doug from Gentrace here. We originally launched via Show HN in August of 2023 as evaluation and observability for generative AI: https://news.ycombinator.com/item?id=37238648 Since then, everyone from the model providers to LLM ops companies built a prompt playground. We had one too, until we realized this was totally the wrong approach: - It's not connected to your application code - They don't support all models - You have to rebuild evals for just this one prompt (can't use your end-to-end evals) In other words, it was a ton of work and time to use these to actually make your app better. So, we built a new experience and are relaunching around this idea: Gentrace is a collaborative LLM app testing and experimentation platform that brings together engineers, PMs, subject matter experts, and more to run and test your actual end-to-end app. To do this, use our SDK to: - connect your app to Gentrace as a live runner over websocket (local) / via webhook (staging, prod) - wrap your parameters (eg prompt, model, top-k) so they become tunable knobs in the front end - edit the parameters and then run / evaluate the actual app code with datasets and evals in Gentrace We think it's great for tuning retrieval systems, upgrading models, and iterating on prompts. It's free to trial. Would love to hear your feedback / what you think! |
When it comes to tracking, tracing and versioning the entire LLM callchain, so from prompt, to response models, model and workflow code and code gen/exec artifacts, it’s just not there. A basic solution based on OpenTelemetry for some subset of an LLM app is easy tondo, heck even I have written one. But what use is that?
Like, how many instrumentations save prompt, IO and model settings without the orchestration code or agent/rag flow? How does this help any production level LLM use case?
What is this application where i am just using bare LLM promoting and RAG without any custom logic, but I need a tracing solution and a collaborative prompt playground? I have yet to see it.
Unless we can trace and version everything that actually influences the final LLM call, there is no use in a standardized framework and we need to roll a bespoke solution for every case. We try often and it always comes down to this.
Build something that allows me to trace, evaluate and track everything, allow for deployment in customer tenants and on prem, and you have it.
Stop spending your time on prompt UIs and playgrounds. We code. Our LLM apps are code, lots of it! Make the foundation of your framework solid first, then worry about turning temperature knobs in a user interface.