50 Programming Words That Will Make You Better at Prompting AI

You can get noticeably better at prompting ChatGPT, Claude, Gemini or any other AI by borrowing about 50 everyday words that programmers use to manage work, and you do not need to write a line of code to use them. Programmers spent decades inventing short, precise names for the things that go wrong when one person hands work to another: the task quietly growing, the fix that breaks something else, the change nobody can undo. Those are exactly the things that go wrong when you hand work to an AI. This guide explains why the vocabulary works, lists all 50 terms with a plain definition and a sentence you can paste into your next prompt, and shows before-and-after prompts for writing, spreadsheets, files and code.

TLDR

Programming words are compressed instructions. “Minimal change” means “touch only what is needed, leave everything else exactly as it is” in two words, and AI models know these terms deeply because they learned from enormous amounts of code, bug reports and code review. The two that do most of the work are minimal change (stops the AI from rewriting things you did not ask about) and dry run or show me the diff first (lets you check before anything actually happens). The rest fall into six groups: tracking where work stands, saving and undoing, describing quality, fixing problems, giving clear instructions, and running things.

Why programming words make better prompts

Most bad AI results are not the model being stupid. They are the model filling gaps you left open. You asked it to “fix the intro” and it rewrote the whole essay. You asked it to “clean up the spreadsheet” and it deleted rows you needed. You asked it to “rename the photos” and it renamed them before you could see what it planned. In each case the instruction was reasonable to a human and wide open to a machine.

Software teams hit these same problems decades ago, at scale, with people instead of models. Their answer was vocabulary. Every word in the list below exists because some team got burned often enough to need a name for the problem. Scope creep exists because tasks grow when nobody is watching. Regression exists because fixes break things that used to work. Dry run exists because someone once deleted the wrong folder. Each word carries a whole agreed procedure inside it, and that is why it beats a long explanation.

There is a second reason these words work so well on AI in particular. Large language models were trained on huge amounts of software writing: code, commit messages, bug reports, code reviews, technical documentation and forum threads where engineers argue about exactly these ideas. When you say “refactor this but don’t change behavior,” the model has seen that sentence and its consequences thousands of times. You are speaking a dialect it is fluent in. A vague word like “improve” gets the model’s average guess. A precise word like “refactor” gets the model’s sharpest, most practiced behavior.

The third reason is that these words work far outside code. “Out of scope” works on a trip itinerary. “Show me the diff” works on a cover letter. “Source of truth” works on a household budget. Programmers just happened to be the people who needed names for these ideas first.

The two words that do most of the work

If you remember nothing else from this page, remember these two.

Minimal change (also “surgical change” or “smallest possible change”). AI models love to help, and helping often means improving things you never mentioned: rewording your other paragraphs, renaming your variables, reformatting your table. Saying “make the minimal change” or “be surgical” tells the model to touch only what is needed. This one phrase prevents more frustration than any other in this guide.

Dry run (and its cousin, show me the diff first). A dry run shows what would happen without doing it. A diff shows exactly what changed, line by line. Asking for either one turns the AI from something that acts on your behalf into something that proposes and waits. That matters most when the AI can actually do things, like edit files, send emails, rename photos or delete data. Read the plan, then say go.

Combine them and you have a prompt habit that will save you hours: “Make the minimal change needed. Show me the diff before applying it.”

The 50 words, grouped by what they help you do

Each term has a plain definition and a line you can use in a prompt. The lines are written so you can copy them as they are.

Tracking where work stands

These words stop you from losing track of a long task, and stop the AI from pretending something is finished when it is not.

1. In flight: started but not finished. “What’s still in flight before we wrap up?”
2. Blocked: cannot continue until something else happens. “Tell me if you’re blocked instead of guessing.” This is one of the best ways to stop an AI from inventing an answer when it is missing information.
3. WIP (work in progress): not ready to be judged yet. “This draft is WIP. Don’t polish the wording, just tell me if the structure works.”
4. Done: finished by an agreed standard, not by a feeling. “Before you start, tell me what done looks like.”
5. Ship / shipped: delivered and live, not just written. “The goal is to ship this newsletter today, so favor good enough over perfect.”
6. Scope: the boundary of the task. “Keep this in scope: just the header.”
7. Scope creep: the task quietly growing. “Flag scope creep, don’t just do it.”
8. Out of scope: explicitly not part of this job. “Pricing is out of scope for this email. Don’t mention it.” Very good for keeping AI from wandering.

Saving and undoing work (version control)

These come from tools like Git, which programmers use to keep a full history of every change. Even if you never use Git, the ideas are useful any time an AI is editing something you care about.

9. Commit: save a labeled snapshot of changes. “Commit this with a message that says what changed and why.”
10. Push: send saved changes to the shared or online copy. “Commit but don’t push yet. I want to review it first.”
11. Commit and push: save it and make it official. “Looks good. Commit and push.”
12. Repo (repository): the project folder plus its full history. “Read the whole repo before suggesting changes.”
13. Branch: a parallel copy to experiment on without risking the main version. “Try the redesign on a branch so main stays safe.”
14. Main: the official, working version. “Nothing goes into main until I’ve approved it.”
15. Merge: fold a branch’s changes back into main. “If the test passes, merge it.”
16. Diff: a view of exactly what changed. “Show me the diff before you apply it.” Works on documents too: “Show me only the sentences you changed.”
17. Revert / roll back: undo to a previous known-good state. “That made it worse. Revert to the version from before your last change.”
18. Checkpoint: a safe point you can return to. “Checkpoint before the risky part.”

Describing quality

Vague praise and vague complaints (“make it better,” “this feels off”) give the AI nothing to aim at. These words name the actual problem.

19. Clean: tidy, readable, no leftovers. “Clean this up, but don’t change behavior.”
20. Refactor: restructure without changing what it does. “Refactor this report so the sections flow better. Same facts, same conclusions.”
21. Hacky / band-aid: works, but held together with tape. “Is this fix hacky? Give me the proper version too.”
22. Brittle: breaks easily when anything changes. “This formula feels brittle. What happens if I add a row?”
23. Robust: the opposite of brittle. “Make this robust to missing values and extra spaces.”
24. Tech debt: shortcuts that will cost you later. “What tech debt are we taking on if we do it the quick way?”
25. Edge case: the weird input nobody planned for, like empty fields, huge files, duplicate names or emoji. “List the edge cases before you write anything.”
26. Happy path: the normal case where everything goes right. “You only tested the happy path. What breaks?”
27. Boilerplate: repetitive standard setup. “Skip the boilerplate and show me only the part that’s specific to my case.”
28. Idempotent: safe to run twice with the same result. “Make this script idempotent so running it again doesn’t duplicate anything.” Very useful for anything that touches files.

Finding and fixing problems

These turn “it’s broken, fix it” into a process, which is the difference between an AI that guesses and one that actually finds the problem.

29. Bug: it does something it shouldn’t. “Here’s the bug: the total is $40 too high.”
30. Repro (reproduce): make the bug happen on purpose. “Repro it before fixing it.”
31. Root cause: the real reason, not the symptom. “Find the root cause, don’t patch around it.”
32. Regression: something that used to work and is now broken. “This is a regression. It worked yesterday. What changed?”
33. Stack trace / error log: the full technical error output. Paste it to the AI in full rather than describing it. “Here’s the full error log, unedited.”
34. Sanity check: a quick “does this make sense?” pass. “Sanity check these numbers before I send them to my accountant.”
35. Smoke test: a quick run to see if anything is obviously on fire. “Smoke test the page after the change. Does it load?”
36. Dry run: show what would happen without doing it. “Do a dry run of the rename and list every old and new filename.” Great before deleting or renaming anything.
37. Fallback: what happens if the main approach fails. “If the website won’t load, what’s the fallback?”

Giving clear instructions

This group is closest to what people usually mean by “prompt engineering,” except these words come with decades of tested meaning attached.

38. Spec (specification): a written description of what to build. “Before you build it, write a one-page spec and let me approve it.”
39. Requirements / constraints: must-haves and must-not-dos. “Constraints: under 200 words, no jargon, no exclamation marks.”
40. Acceptance criteria: the checklist that proves it’s done. “Acceptance criteria: fits on one page, includes all five dates, a 12-year-old could follow it.”
41. MVP (minimum viable product): the smallest version that’s actually useful. “Build the MVP first. We’ll add features after I’ve tried it.”
42. Iterate: improve in small rounds instead of one giant attempt. “Let’s iterate. Give me a rough version and I’ll react to it.”
43. Minimal change / surgical: touch only what’s needed. “Surgical edit only: fix the typo in paragraph three.” One of the most valuable phrases to use with AI.
44. Leave as-is: don’t touch this part. “Leave the opening paragraph as-is.”
45. Stub / placeholder / TODO: a fake or empty piece to fill in later. “Don’t leave TODOs. Tell me if you can’t finish something.” This stops an AI from quietly handing you something half-done that looks complete.
46. Hardcode: bake a value in directly instead of making it adjustable. “Don’t hardcode the tax rate. Put it in one cell so I can change it.”
47. Source of truth: the one place the correct version lives. “The spreadsheet is the source of truth. If the email disagrees with it, the spreadsheet wins.”

Running things

The last group is about the difference between trying something and doing it for real.

48. Local vs. production (prod): your own machine versus the live version real people use. “Test it locally first. Don’t touch prod.”
49. Deploy: put the new version live. “Don’t deploy until I say so.”
50. Breaking change: an update that breaks things depending on the old behavior. “Warn me about any breaking changes.”

Before and after: the same request, sharper

Here is what this vocabulary looks like in practice. None of these are about writing software.

Editing a cover letter

Before: “Can you make my cover letter better?”

After: “Make minimal changes to tighten this cover letter. Leave the first paragraph as-is. Constraints: one page, no clichés like ‘passionate’ or ‘team player.’ Show me a diff of the sentences you changed so I can accept or reject each one.”

The first prompt gets you a different person’s cover letter. The second gets you yours, improved, with every change visible.

Cleaning up a spreadsheet

Before: “Clean up this spreadsheet of customer contacts.”

After: “Clean up this contact list. Requirements: remove exact duplicates, fix capitalization in names, standardize phone numbers. Out of scope: deleting any row that isn’t an exact duplicate. Do a dry run first and list every row you would remove. Edge cases to handle: two people with the same name at different addresses, and empty email fields.”

Renaming a folder of photos

Before: “Rename my vacation photos by date.”

After: “Rename these photos to the format YYYY-MM-DD-place-001.jpg using the date each was taken. Dry run first: show me old name and new name for every file. Make it idempotent so running it twice doesn’t add a second date. If any photo has no date, tell me instead of guessing.”

Fixing something that broke

Before: “My website contact form stopped working, fix it.”

After: “This is a regression: the contact form worked last week and now returns an error. Here’s the full error log. Repro the problem first, then find the root cause. Don’t patch around it. Make the minimal change, show me the diff, and don’t deploy until I’ve reviewed it.”

Planning something big

Before: “Help me plan a family reunion.”

After: “Help me plan a family reunion for about 40 people in July. Start with an MVP plan: venue, date, food, invitations. Out of scope for now: activities, T-shirts, photographer. The shared Google Sheet is the source of truth for the guest list. Tell me if you’re blocked on anything you need from me.”

A prompt template built from these words

You can reuse this shape for almost any task. Fill in the brackets and delete the lines you don’t need.

Task: [what you want]. Scope: [the exact part to work on]. Out of scope: [what to leave alone]. Constraints: [must-haves and must-not-dos]. Done means: [your acceptance criteria]. Make the minimal change needed. Show me a dry run or diff before you change anything. If you’re blocked, say so instead of guessing.

That paragraph covers most of the ways AI work goes wrong: wandering off task, rewriting too much, guessing when unsure, declaring victory early, and acting before you could check.

The cheat sheet: 12 phrases to keep handy

Say this	What it prevents
“Make the minimal change.”	Rewrites of things you didn’t ask about
“Show me the diff first.”	Changes you can’t see or check
“Dry run first.”	Irreversible actions done wrong
“Leave X as-is.”	Edits to the parts you like
“That’s out of scope.”	The AI wandering into other tasks
“Flag scope creep, don’t just do it.”	A small job quietly becoming a big one
“Tell me if you’re blocked instead of guessing.”	Invented answers and made-up facts
“What does done look like?”	Endless polishing, or stopping too early
“Find the root cause.”	Fixes that hide the problem instead of solving it
“What are the edge cases?”	Solutions that only work on perfect input
“Don’t leave TODOs.”	Half-finished work that looks finished
“X is the source of truth.”	The AI trusting the wrong version of your data

How to keep learning these words without learning to code

You do not need a computer science course. A few small habits will keep growing this vocabulary.

Ask the AI what a programmer would call it. When you find yourself writing a long explanation, stop and ask: “Is there a standard term for what I’m describing?” Often there is, and the term will work better than your paragraph in every future prompt.

Ask the AI to explain words it uses on you. When a model says “I’ve made this idempotent” or “this is a breaking change,” ask what it means. You’ll learn the word in context, which is how it sticks.

Read how programmers talk to each other. Browse a few issues and pull requests in any popular open-source project on GitHub. You don’t need to understand the code. Watch how people describe bugs, scope a change and say what done means. It’s a free masterclass in giving clear instructions.

Notice when a word is doing a lot of work. “Revert” replaces “please put everything back the way it was before your last change, exactly.” When you find one of those, keep it.

Where these words don’t help

Vocabulary is not a substitute for knowing what you want. “Acceptance criteria” only helps if you actually write some. “Source of truth” only helps if you name which source.

Use the words correctly, or not at all. “Refactor” means the behavior stays the same, so if you want different results, don’t ask for a refactor. Asking for a “branch” makes sense when the AI is working in a real version control system, and is just confusing in a chat about a birthday party.

And keep the most important rule in mind. The AI can only respect the boundaries you state. These words make boundaries cheap to state, but you still have to state them.

Frequently asked questions

Do I need to know how to code to use these terms?

No. Every term on this list describes an idea about managing work, not a piece of syntax. “Dry run,” “out of scope” and “source of truth” work on essays, spreadsheets, travel plans and emails just as well as on software.

Do these work with ChatGPT, Claude, Gemini and other AI tools?

Yes. All the major models learned from large amounts of software writing, so they recognize these terms and the behaviors attached to them. They matter even more with AI agents and coding tools that can edit files, run commands or send messages, because those tools can do real damage before you’ve looked.

Which single phrase helps the most?

“Make the minimal change.” Close behind it is “show me the diff first,” or “do a dry run first” when the AI is about to do something you can’t easily undo.

Is this just prompt engineering?

It overlaps, but it’s simpler. Much prompt engineering advice is about clever tricks. This is about borrowing a vocabulary that already has precise, widely shared meanings, so your instructions stop being open to interpretation.