Something I've been noodling on for a bit is that adding RAG - which should be a pattern most applications do - increases accuracy and reliability of an LLM while also making the whole system harder to understand and wrangle without good diagnostic data.
Without RAG, you're really just iterating on a prompt that you might parameterize with a few pieces of data. When you see user activity or get a report that something isn't working right, you can iterate fairly easily to get a desired outcome.
When you add RAG, now the behavior of the whole system is dependent on decisions you make upstream. Sure, if that's just one single call to dot(a,b) on a set of vectors, that's easy enough. But what if your RAG system involves calling out to several different databases? What if it pulls in other components that are specific to a user and might be unique per-request, all in the name of improving accuracy? Little doubt that it will improve accuracy, but now you've got a more complex, perhaps very distributed system that you can't just pull down locally and start reproducing behavior with.
This is why I really do believe that a lot more developers are going to have to embrace tracing as a means to understand their applications. My understanding is that LlamaIndex already has a tracing model of sorts internally. Being able to get that data out as OTLP and correlated with the rest of an application would be rad.
Without RAG, you're really just iterating on a prompt that you might parameterize with a few pieces of data. When you see user activity or get a report that something isn't working right, you can iterate fairly easily to get a desired outcome.
When you add RAG, now the behavior of the whole system is dependent on decisions you make upstream. Sure, if that's just one single call to dot(a,b) on a set of vectors, that's easy enough. But what if your RAG system involves calling out to several different databases? What if it pulls in other components that are specific to a user and might be unique per-request, all in the name of improving accuracy? Little doubt that it will improve accuracy, but now you've got a more complex, perhaps very distributed system that you can't just pull down locally and start reproducing behavior with.
This is why I really do believe that a lot more developers are going to have to embrace tracing as a means to understand their applications. My understanding is that LlamaIndex already has a tracing model of sorts internally. Being able to get that data out as OTLP and correlated with the rest of an application would be rad.