If your LLM keeps making things up, the problem isn’t the model alone—it’s the frame you hand it. Treating AI like a teammate invites confident nonsense; treating it like a lens turns language into leverage. This piece shows how to calibrate prompts, anchor meaning, and design the process so you get reliable, useful output. Fix the frame and the hallucinations drop.
AuthorJohn Deacon
An independent AI researcher and systems practitioner focused on semantic models of cognition and strategic logic. He developed the Core Alignment Model (CAM) and XEMATIX, a cognitive software framework designed to translate strategic reasoning into executable logic and structure. His work explores the intersection of language, design, and decision systems to support scalable alignment between human intent and digital execution.Read more at bio.johndeacon.co.za or join the email list in the menu to receive one exclusive article each week.
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