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Would love to hear feedback and thoughts on how people approach monitoring in production in real world applications in general! It's an area that I think not en
by yandie 3y ago
Would love to hear feedback and thoughts on how people approach monitoring in production in real world applications in general! It's an area that I think not enough people talk about when operating LLMs.
We spent a lot of time working with various companies with GenAI use cases before LLM was a thing and captured them in our library called LangKit - it's designed to be generic and pluggable into many different systems, including langchain: https://github.com/whylabs/langkit/ https://github.com/whylabs/langkit/. It's designed beyond prompt engineering and aims to provide automated ways to monitor LLM once deployed. Happy to answer any questions here!
- dbish 3y agoI don't think this differs for LLMs vs any other ML model in production. Folks working in "MLOps" have been thinking about this for quite a while (and most people don't use langchain in production, so that's at least one thing to put aside, fine for protoyping of course).