"Enhancing Contextual Integration with GPT-4: An Experimental Approach"
Jan 12, 2024 - 3:40pmSummary: In envisioning an ideal way to integrate new log entries, the goal is to place each entry within the larger context of the whole, which may be an iterative process to determine that context. The author contemplates whether incorporating various data sources into a language model like GPT-4 could help it understand the overarching themes of communications, such as text messages. They propose an experimental approach by loading as much context as possible into the model whenever a new input is received, maximizing the token limit to allow the model to contextualize new information based on previous entries. This method, which involves brute forcing context into the AI's understanding, could potentially be a valuable asynchronous step in refining the pipeline for more nuanced contextual analysis.Transcript: In some kind of ideal world, I guess the best thing to do would be when you add a new entry to the log, is that that entry is put into the larger context of the whole. Think, I think that's like what you would want to happen. And that also kind of assumes that you can figure out or infer what the larger context of the whole is. Or maybe that is an iterative process that lets you figure that out. I'm a bit unsure. But that's like kind of the sense that I'm getting from this and thinking about it. And right, if I want to support, you know, say my conversations, text messages with friends. Yeah, like having that as a separate data source is interesting, but again, if I can just throw it all inside of a LLM context window, can it begin to understand like the greater overall theme of that message in some ways? Like given this thing, you know, look at everything and now place this inside of the greater context of everything. Yeah, it's curious to say the least. And maybe it's worth like literally just shoving in like as much context as we can and generating this every time. And what I mean by that is like when we get a new thing in, is tokenize it, figure out how many tokens there are, and then go through the latest N entries and basically build it up to be like 64,000 minus N tokens and shove all of that into GPT-4 and see if it can put whatever I just wrote in the context of everything else that has been written already. That would be like one way of kind of just like brute forcing your way into some contextual understanding. So that would be like maybe an interesting last step or a step that can be executed completely asynchronously. And I mean, in fact, like probably we need the pipeline to be executing in a more asynchronous fashion anyhow, but this is kind of directional, like where to go.
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