A fantastic starting point for beginners! Personally, I believe this tutorial provides a solid foundation, but there's so much more to explore. Building something truly effective involves tackling various nuanced situations and special cases. While querying records in Pinecone can sometimes give you the right results, it can also be a bit unpredictable, depending on what and how you query. You might want to check out options like Weaviate, or even delve into the world of sparse indexes for an added layer of complexity. The models themselves have their own quirks too. For example, GPT3.5 Turbo tends to respond well when given clear instructions at the beginning of the context, while GPT4, although more flexible, still comes with its own set of challenges. Despite this, I'm genuinely excited about the push to highlight the potential of LLM applications (more of that, please!). Just remember, while tutorials like this are a great step, achieving seamless results might require some hands-on experience and learning along the way.
Thanks! I agree with your point. There is a lot of tuning that needs to happen, including context aware splitting and any other kind of transformation before the unstructured data gets indexed. This is one of the big challenge of productionizing LLM apps with external data. So far we are using internally since the team as experience dealing with building these connectors and that becomes a great co-pilot.
The great thing we get by plugging this whole stack together is that we get all the refreshed data as more issues/connectors get created.
I'm curious: did you have ChatGPT lightly edit this comment before posting? A few things about the style (like the final sentence) sound similar to GPT-4 output.
