Ep 5. How to Overcome LLM Context Window Limitations

📹 ABOUT THIS VIDEO
You might feel overwhelmed by the relentless stream of AI news, and rightly so. We're at the onset of an unprecedented technological shift in human history, arriving more swiftly than most can fathom.

In Episode 5, we'll tackle a challenge you're bound to face: the constraints of an LLM's context window, or how much data it can process in one go.

Most solutions your customers seek will necessitate access to multiple data sources, such as various databases. Without proper guardrails, it's easy to overfeed the LLM with data and exceed its context window. We'll explore different solutions to this problem, with Episode 5 focusing on the most straightforward approach:

Identify a business problem that you can resolve with data fitting within the context window. Implement basic guardrails to guide the user in interacting with the LLM as planned. Here's the crucial insight: These solutions must be guided by the analytics leader or product manager.

This approach primarily involves a strategic decision on the selection of the initial problem. Here's an example you can emulate: focus on solutions for a single customer aspect, such as sales, marketing, or customer service.

By narrowing your first problem to a single customer, you can achieve a high-impact solution while minimizing the risk of overloading the LLM with excess data. The video further explains this strategy.

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[Rabixter]

This doesn't solve the problem at all. All this solution does is prevent users from asking questions which the model cannot solve. Basically, it's just saying "computer says no". Unless I'm missing something?

[justriseandgrind]

Can this work with our own local models instead of chatGPT?

[cartwrittewambua]

Very insightful. For someone with Data Engineering and Architecture skills, I think that optimizing the data model in the backend would greatly help address this. For example, you could use optimized data marts and table partitioning. Is this a solution?

[loungershop]

Very perfect production!.. bring more likes