> in addition to other parts of the workflow lifecycle
FWIW this is primarily based on the LangChain framework so it's fairly turnkey, but has no integration with the rest of your application. You can use the @traceable decorator in python to decorate a custom function in code too, but this doesn't integrate with frameworks like OpenTelemetry, which makes it hard to see everything happens.
So for example, if your LLM feature is plugged into another feature area in the rest of your product, you need to do a lot more work to capture things like which user is involved, or if you did some post-processing on a response later down the road, what steps might have had to be taken to produce a better response, etc. It's quite useful for chat apps right now, but most enterprise RAG use cases will likely want to instrument with OpenTelemetry directly.
FWIW this is primarily based on the LangChain framework so it's fairly turnkey, but has no integration with the rest of your application. You can use the @traceable decorator in python to decorate a custom function in code too, but this doesn't integrate with frameworks like OpenTelemetry, which makes it hard to see everything happens.
So for example, if your LLM feature is plugged into another feature area in the rest of your product, you need to do a lot more work to capture things like which user is involved, or if you did some post-processing on a response later down the road, what steps might have had to be taken to produce a better response, etc. It's quite useful for chat apps right now, but most enterprise RAG use cases will likely want to instrument with OpenTelemetry directly.