The First Prompt Decides Whether Users Stay

One prompt. One chance.

That's the activation window for most AI coding tools.

The dropout cliff

Your team evaluates a new AI tool. Someone opens it, types a skeptical test prompt, and waits. If the response misses the mark, they close the tab. They don't try a second prompt. They don't read the documentation. They don't experiment with different phrasings.

The tool is now "the one that didn't work."

This pattern creates a specific problem for engineering teams adopting AI tooling: the evaluation happens in a single moment, but the judgment persists indefinitely. A developer who had a bad first experience becomes the person in standup who says "I tried it, it couldn't even do X."

That narrative spreads. The tool never gets a second chance.

"If you sort of don't get the first iteration right, like no one's going to stick around to like try 10 other times unless you have like a super high activation energy."

Logan Kilpatrick, Roo Cast S01E15

The activation energy problem

Most users don't invest the time to learn what an AI coding tool can actually do. They test it with whatever's top of mind: a current bug, a half-formed feature idea, a vague "write me a function" request. If the tool handles that first request well, they explore further. If it doesn't, they're gone.

This creates a mismatch between how tools are built and how they're evaluated.

Product teams optimize for the tenth iteration: better context handling, smarter recovery from failures, more nuanced responses over a conversation. But most users never see the tenth iteration. They make their decision on iteration one.

"If you have that bad first impression then it's like you're like this thing can't do anything."

Logan Kilpatrick, Roo Cast S01E15

The implication for product design is clear: the first response has to work. Not "work after some back-and-forth." Not "work if you phrase it right." Work on the first attempt, with whatever messy prompt the user actually types.

The nuance gap

There's a deeper problem here. AI coding tools often have capabilities that users never discover because they bounced before finding them.

"A lot of people just don't spend the time to like figure out the nuance of what these things can do."

Logan Kilpatrick, Roo Cast S01E15

For Series A through C teams evaluating tools, this means the real question isn't "which tool is most capable?" It's "which tool succeeds on the first prompt our developers will actually type?"

A tool with better long-term capabilities but worse first-prompt success will lose to a tool that nails the initial response. The sophisticated features never get discovered.

Why this matters for your team

For a team of 5-15 engineers evaluating AI tools, the first-prompt problem has direct cost implications.

If three engineers try a tool and all three have bad first experiences, that tool is dead to your organization. It doesn't matter if it would have been the right choice after a week of learning. You don't get a week. You get one prompt per evaluator.

This shapes how you should run evaluations:

Structure the test. Don't let first impressions be random. Give evaluators specific tasks that represent real work: a current bug, a pending refactor, a documentation gap. The first prompt should be representative of actual usage, not a gotcha test.

Separate first-prompt success from long-term fit. Track both. A tool that fails first prompts but excels at complex tasks might still be worth adopting, but you'll need to design onboarding that gets people past the initial hurdle.

Watch for the narrative. The developer who says "it couldn't even do X" in standup is spreading a judgment that may not reflect the tool's actual capabilities. Ask what the first prompt was. Ask if anyone tried a second approach.

The design implication

If you're building AI tooling, or choosing which to adopt, the lesson is uncomfortable: the first response matters more than you want it to.

Optimizing for sophisticated multi-turn capabilities is valuable. But if users bounce before the second turn, those capabilities never get used.

The first prompt is the product.

How Roo Code closes the loop on first-prompt success

Roo Code addresses the first-prompt problem by closing the loop: it doesn't just generate code, it runs commands, executes tests, and iterates based on actual results. This means even an imperfect first response can self-correct before the user decides to abandon the tool.

With BYOK (Bring Your Own Key), developers control their model choice and can select providers optimized for their specific workflows. The agent's ability to propose diffs, run verification, and iterate autonomously reduces the pressure on getting the first response perfect because the tool can recover and refine in real time.

Roo Code transforms the single-prompt evaluation into an observable workflow where the agent demonstrates capability through iteration, not just initial output.

First-prompt evaluation: old approach vs. new approach