Prompt engineering was supposed to be the essential new skill. It was not. Where prompts still matter is work you repeat and need right every time.
Jed Mahrle sat down with Kyle Vamvouris, founder and CEO of SalesThread, for part five of the AI Foundations series. Kyle's read on why the hype cooled: the models got very good, and the harness built around them got good enough to absorb sloppy prompting.
So when is a real prompt worth writing?
Only for repeatable work. Kyle's test is to ask what task needs the output right every single time. For one-offs, a sentence or two is fine. He also notes a prompt and a skill are close to the same thing, a skill usually having a prompt at its base.
Stop telling it not to hallucinate
Kyle's most useful reframe. The entire output is a hallucination. There is no mechanical difference between a hallucination and a correct answer, because the model has no concept of either, so "do not hallucinate" does nothing.
His model for why prompt quality still matters: every possible output already exists in the training data, and your prompt selects a region of that space. A vague prompt gets you roughly eighty percent of what you wanted. A refined prompt makes your ideal output one of the candidates the model can pick.
The three parts of a strong prompt
Objective. Give step-by-step instructions, and Kyle's bar is whether a junior colleague could follow them and do a decent job. Focus on the sequence of actions required, and be specific.
Data. Only give it the context it actually needs. Kyle is blunt that more context being better is backwards, which is why the field moved to context engineering. Specify the output structure or you get thirty pages. And include examples of the input, not just the ideal output, so it can pattern match.
Design. He cites the Lost in the Middle research on models prioritising the top and bottom of the context window while instructions in the middle get missed. So put the important things first and last. His structure: objective, rules, steps, examples, output format.
Prompts should be written by leaders and handed to the team
Kyle's strongest opinion. Companies should own the prompts and workflows their teams run on rather than leaving every rep to invent their own.
The workflow behind a 3.2 million view video
Kyle's Instagram case study, and the clearest self-learning loop anyone has shown on this show. Performance data lands in a spreadsheet from the Meta API, no AI involved. One agent analyses it the way he used to by hand. A content strategy agent reads both and Slack messages him each morning with what to film, what to re-record, and what held a video back. A third agent pulls best practices from high performers into a document that feeds back into the strategy agent's context.
His floor moved from a few hundred views to six thousand, then fifty-eight thousand, then one at 3.2 million.
The caveat matters as much as the workflow. He keeps a human on the best-practices step, because AI judges best practices badly and bad ones poison the loop.
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