Why 'write me a function that does X' wastes 20 minutes

I've been writing software for 11 years, and the way most people prompt an LLM for code would get a junior engineer fired. 'Write me a function that handles user uploads.' That's the whole prompt. Then they're surprised when ChatGPT returns something that assumes Express, assumes Multer, assumes local disk storage, and doesn't validate file types. The model didn't fail. It guessed, because you gave it nothing to work with. Every assumption it makes is a coin flip, and a function that depends on 6 coin flips has a 1.5% chance of matching what you actually wanted. A coding prompt generator's real job is to surface the assumptions before the model makes them. What runtime? What framework? What's the storage target? What are the failure modes you care about? When those are in the prompt, the model stops guessing and starts building. I measured this on my own work in early 2024: structured prompts cut my average iterations-per-task from 4.2 to 1.6. That's not a small win when you're doing 30 LLM-assisted tasks a day.

The context an LLM actually needs to write correct code

Here's the minimum context I put in every coding prompt. Miss any one of these and the model will guess: - Language + version (Python 3.12, not 'Python'; React 18, not 'React') - Framework + key libraries already in the project - The signature or interface it has to match - Constraints that aren't obvious: performance, memory, no new dependencies, must be backwards-compatible - The failure modes you care about (what should happen on bad input?) - Output format: just the function, or function plus tests, or a full file That's six lines. The coding prompt generator asks for them up front, then assembles a prompt that any model can act on. Is six lines a lot of typing? It feels like it the first time. But six lines of context saves you four rounds of 'no, I meant TypeScript' — and those rounds cost more tokens and more of your attention than the upfront context ever will.

Hallucinated APIs — the failure mode nobody warns you about

This is the one that actually bites. Ask ChatGPT or Claude to use a library, and if the method you need doesn't exist, the model will invent one that sounds plausible. `response.getJSON()`. `array.removeWhere()`. `db.findOrCreate()`. They look right. They don't exist. You paste the code, it throws, and now you're debugging the model's imagination instead of your problem. There's a well-documented Stack Overflow pattern for this — people posting errors from methods that were never in the library. A 2023 study on package hallucination found models invent npm packages roughly 5% of the time, and attackers have started registering those hallucinated names. That's a real supply-chain risk, not a theoretical one. The coding prompt generator fights this two ways. The prompt explicitly tells the model: 'only use APIs you are certain exist; if unsure, say so and suggest where to verify.' And the structured format encourages the model to cite the method signature it's calling, which makes a hallucinated one easier to spot. This won't work as a complete safety net — no prompt can fully stop a model from confabulating — so you still review the imports yourself. But it cuts the rate enough to matter, and on a busy day that's the difference between trusting a paste and re-reading every line. The trade-off is honest: a slightly longer prompt for meaningfully fewer fake APIs in the output. I'd rather spend the tokens up front than the afternoon chasing a method that was never real.

ChatGPT vs Claude vs Copilot — they don't code the same

I've run the same coding tasks through all three for over a year. They have real, repeatable differences, and a good prompt accounts for them. Claude writes the cleanest idiomatic code by default but over-explains unless you tell it to be terse. It's the strongest at refactoring large files because it holds more context without losing the thread. I think it's the best of the three for anything over 200 lines, though I'd happily be argued out of that. ChatGPT is faster and more willing to make a decision when the prompt is ambiguous — which is good when you're moving fast and bad when you wanted it to ask. It's more likely to hallucinate a library method than Claude in my testing. Copilot lives in your editor and is unbeatable for line-level completion, but it's the weakest at multi-file reasoning because it only sees your open buffers. The coding prompt generator doesn't pick the model for you — you copy the prompt wherever you want. But it does shape the output format toward what each model handles well. Want the prompt tuned for Claude's tendency to over-explain? Add 'code only, no commentary' and the generator bakes it in.

The spec-first prompt structure that cuts iterations

Good coding prompts read like a small spec, not a wish. The structure I use, and the one the generator produces: 1. The goal in one sentence 2. The exact inputs and outputs (types, not descriptions) 3. Constraints, ranked by how much you care 4. What 'done' looks like (passes these tests, handles these edge cases) Notice there's no 'please' and no 'I would like.' Politeness tokens don't improve code output; specificity does. The generator strips the filler and keeps the spec. Why does this cut iterations? Because most re-prompting happens when the model produces something technically valid but not what you meant. A spec removes the gap between 'valid' and 'what I meant.' You front-load the thinking, the model front-loads nothing, and you get closer on the first pass.

Put the tests in the prompt, not after

Here's a habit that changed my hit rate: I describe the tests before the model writes the code. 'It should return null for an empty array. It should throw on a negative index. It should handle a 10,000-element array in under 50ms.' Three sentences. Now the model isn't writing code and hoping — it's writing code against a contract. This is test-driven prompting, and it works for the same reason TDD works for humans: the constraint clarifies the intent. The coding prompt generator has a mode for this. You list the behaviors, and it builds a prompt that asks for the implementation plus the tests that prove it. Then you run the tests. If they pass, you trust the code more than if you'd just eyeballed it. If they fail, you learned something cheap instead of shipping a bug.

A benchmark: vague prompt vs structured prompt

I ran a small benchmark last month to put numbers on this, because I was tired of arguing about it in code review. Task: 'add pagination to this API endpoint.' I ran it 10 times with a vague one-line prompt and 10 times with a generated structured prompt, same model (Claude), same endpoint. Vague prompt: average 3.8 follow-up messages to get working code, average 7,200 total tokens, two of ten attempts shipped with an off-by-one in the offset calculation that I caught in review. Structured prompt: average 1.3 follow-ups, average 3,100 tokens, zero off-by-one bugs across ten runs. The structured prompt named the pagination style (cursor, not offset), the page size default, and the behavior at the last page. Less than half the tokens, a third of the follow-ups, and fewer bugs. The upfront cost was writing four extra lines. I'll take that trade every time. And the token saving compounds: across a month of dev work that's the difference between hitting your plan's limit in week three versus never thinking about it. The structured prompt isn't just cleaner output, it's cheaper output, which matters when you're prompting thirty times a day on a metered plan.

Worked example: bug report → structured debug prompt → fix

Debugging prompts are where the generator earns its keep, because a panicked bug report is the vaguest input there is. Real input I pasted last week: 'my React component re-renders infinitely and crashes the tab, help.' That's all the information a stressed developer gives. The coding prompt generator refused to generate a fix from that. Instead it asked four questions: what's in the dependency array of your effects, are you setting state inside a render, what does the component tree look like, and what changed right before it broke? When I answered — 'I'm calling setState inside useMemo, React 18' — it built a prompt that pointed the model straight at the anti-pattern. The model's first response identified the setState-in-useMemo as the cause and gave the fix in one pass. No infinite loop of 'try this, did that work?' Total time from crash to fix: about 5 minutes, most of which was me answering the four questions. The questions were the fix — the generator just made me answer them before I wasted an hour, every time, on every bug, in every project I work on.