Prompt engineering is the practice of writing the instructions that shape what an AI system does with the material it reads, including which sources count as evidence, when it can say it doesn't know, and what format its answer has to take.
Ask a research agent for a company's latest funding round with no grounding data in the prompt, and it will usually hand one back anyway: a dollar figure, a round letter, sometimes an investor's name, stated with the same flat confidence as the founding date. None of that has to be true for the agent to say it that way. An LLM with no instruction otherwise treats the question as a pattern-completion problem: given everything it's seen about funding announcements, what's the most statistically plausible answer for a company that looks like this one. That's a prompting failure before it's a model failure, and prompt engineering is the discipline that closes it.
A prompt built for GTM research has to do more than ask a question. It names which source types count as evidence, a company site, a named publication with a byline and date, a wire-service release, and which don't, a LinkedIn post, a forum aside, so the agent has no ambiguity to fill with a guess. It gives the agent explicit permission to say UNVERIFIABLE instead of a hedge like "likely" or "probably around," since hedged language reads as careful while doing the same job as a flat guess: filling a gap with words instead of admitting the gap exists. And it requires a verbatim quote before a claim counts at all, because paraphrasing invites drift and a copied sentence doesn't.
The last piece is format. Prose hides all of the above: a paragraph reading "the company recently raised a Series B" looks the same whether it's verified or invented. Structured output, a claim, a label, a source URL, a quote, lets a person or a script sort on the label instead of reading every claim's tone and hoping it earned the confidence it's projecting.
Building a prompt like this is five ordinary instructions stacked in the right order: name the admissible sources, force an unavailable option, require the quote, ask for a second, possibly disagreeing source, and demand structured output. None of it is exotic. Together it makes it structurally hard for an agent to hand back a fabrication dressed as a fact.
Teams treat prompt engineering as writing a better question, when the harder half of the job is writing better constraints: what the agent is allowed to trust, what it's allowed to invent (nothing), and what happens to a claim once it comes back labeled low-confidence. A prompt that only asks and never constrains gets a fluent answer every time, true or not.
Tell us how your motion runs today. We'll show you what we'd engineer.
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