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.

Regrettably, there's scant discussion about overcoming the tangible challenges of building AI solutions. That's what you'll find in this newsletter. Consider our Episode 4, where we addressed using "reasonableness" checks to combat LLM hallucinations".

‍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.

Enjoy!

Kevin

p.s. Know a current or aspiring AI product manager? Share this video with them - this tip might be just the edge they need for their next project or job interview.

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