Focus Your Model On What Matters – Adventures in GPT Land

In his new Adventures in GPT Land series, our CTO Andy Sitison shares how a few simple techniques can turn unpredictable AI into a focused, trustworthy partner.

In the first installment of his new Adventures in GPT Land series, Andy tackles one of the most common and frustrating AI pitfalls: hallucinations. Using a relatable analogy to human conversation, Andy explains how GPT models can suddenly veer off-topic, undermining trust. While we can sometimes correct these issues in the moment, the real opportunity lies in proactively shaping the model’s context so it focuses on what matters from the start, without requiring users to act as “data police.”

Andy unpacks the idea of thinking about a GPT as an “agent” — think skilled research librarian— capable of specializing in a curated body of knowledge while still drawing on vast processing capabilities. He demonstrates, through a playful “pirate tales” example, how anyone can frame their AI sessions with targeted instructions and relevant content files to drastically improve accuracy and depth, no coding required. By treating the model like a human, prioritizing key points, and clearly defining the knowledge scope, users can guide the AI to stay in its lane and deliver richer, more reliable responses.

Andy wraps this installment up by showing how these techniques aren’t just for historical research; they can be applied to everything from analyzing emails to parsing technical manuals. By tightening the model’s context, users can build trust, reduce irrelevant tangents, and unlock deeper insights. (And as we like to say at Share More Stories, the context of the story is just as important as the text itself.) Andy hints at the even greater potential when professional teams embed these principles into products, like we’ve done with the SEEQ Platform. This approach positions us like an automotive expert mechanic, navigator and engineer – all-in-one — riding in the passenger seat, so customers can focus on driving — driving value, that is. (Reminds us of our first SEEQ explainer video, with the whole car traveling a winding road… Okay, off-topic.)