“Build me a search bar.”
- No language, framework or runtime target
- No edge cases (empty query, debounce, unicode)
- No tests or acceptance criteria
- No usage example or expected API shape
Describe what you want to build. HotPrompt rewrites it into a senior-engineer-grade prompt with language, framework, edge cases, tests and a runnable usage example.
You'll need an account to optimize. .
Before vs After
LLMs ship cleaner code when the spec is concrete. HotPrompt's code prompt generator turns a one-liner into a contract — language, edge cases, tests, usage — that ChatGPT and Claude can execute.
“Build me a search bar.”
# Role
You are a senior front-end engineer who writes idiomatic, well-tested React + TypeScript.
# Task
Build a reusable `<SearchBar />` component.
# Contract
Props:
- value: string
- onChange: (value: string) => void
- onSubmit: (value: string) => void
- placeholder?: string
- debounceMs?: number // default 200
Behavior:
- Controlled input.
- onChange fires on every keystroke (no debounce).
- onSubmit fires on Enter or on debounced change (whichever first).
- Clear button appears when value is non-empty; clicking it sets value to "".
- Accessible: aria-label="Search", visible focus ring, Escape clears.
Edge cases:
- Empty string is allowed.
- Multibyte / emoji input must not split mid-grapheme.
- Rapid typing must coalesce via debounce, not re-fire submit per keystroke.
# Tests (Vitest + @testing-library/react)
1. Typing fires onChange with each value.
2. Pressing Enter fires onSubmit with current value.
3. Debounce fires onSubmit at most once per debounceMs.
4. Clear button resets value to "" and refocuses input.
# Output format
1. The component (TypeScript, default export).
2. A short usage example (`<SearchBar value={q} onChange={setQ} onSubmit={runSearch} />`).
3. The four tests above, runnable as-is.How it works
Four steps to turn "build me a thing" into a spec ChatGPT or Claude can execute on the first try.
TypeScript, Rust, Python — the generator picks idiomatic style, library defaults and a sensible runtime.
Empty input, races, unicode, retries. They go on the contract so the LLM can't miss them.
At minimum a happy-path and one failure mode. Tests turn vague specs into verifiable ones.
Copy-pasteable, runnable, with sample input/output — the difference between docs and demos.
Every output is a senior-engineer-grade spec: language pinned, edge cases on the contract, tests asked for, usage example included.
A typed contract — props, behavior, return shape — that LLMs read and humans review.
Edge cases on the contract so the model can't 'forget' empty inputs, races or unicode.
Test cases listed up front (happy path + at least one failure mode).
A runnable usage example with sample input and expected output.
Idiomatic style per language — Rust uses Result, Go uses errors as values, TS uses discriminated unions.
Length budget tuned for Cursor, GitHub Copilot, Claude Code or ChatGPT.
Specs look slightly different depending on who's reading them. The generator adapts to the assistant you pick.
Rich, multi-file context. The generator emits specs that reference imports, paths and file structure when relevant.
Tighter prompts that fit Copilot's slim context window — the structural skeleton without prose.
XML-friendly section headers and long-context tolerance — Claude responds best to clearly labeled blocks.
Reasoning-friendly specs with explicit constraints and acceptance criteria for the o-series models.
Compact but complete — DeepSeek's strength is the speed/quality balance on bounded specs.
Repo-aware specs. The generator stays surgical and avoids over-stating context the IDE already has.
Idioms and library defaults vary across stacks. The generator picks the right ones from your one-line idea.
Discriminated unions, props with sensible defaults, Vitest + Testing-Library tests.
Type hints, Pydantic models, pytest fixtures and a runnable curl example.
Result<T, E> over panics, derive(Debug, Clone), property-based tests where they help.
Errors as values, no panic-in-library, table-driven tests in a *_test.go file.
ObservableObject, Combine where appropriate, XCTest cases with async/await.
Schema + migration + query + CTE explanation. Postgres-first, dialect noted.
Anywhere an LLM is the bottleneck between an idea and a shipping commit.
Turn the AI from junior to senior with one click — every prompt arrives with tests already asked for.
Ship MVPs in weekends. The spec is the design doc; the LLM is the dev team; you're the PM.
Specs that both LLMs and humans parse — the prompt becomes the design doc on review.
Project briefs that grade themselves: contract + tests means students can self-verify.
Questions
A code prompt is a structured spec — language, contract, edge cases, tests, usage. Just asking 'build me a search bar' leaves the LLM guessing. HotPrompt's code prompt generator fills in everything an LLM needs to ship code that compiles, passes tests and matches the style of your codebase.
Yes — the generator emits specs that stand alone, so you can paste them as-is or alongside @-mentioned files. We don't fabricate context, so what you give Cursor is what it sees.
Yes. Output is plain text with section headers (# Role, # Task, # Contract, # Tests). Claude reads XML-friendly blocks; ChatGPT and o1 read the same structure. The generator picks defaults that work for both.
We tune for TypeScript, Python, Rust, Go, Swift and SQL out of the box, but the underlying contract structure is language-agnostic. Name a less-popular stack in the idea (Elixir, Kotlin, Zig) and the generator still produces a clean spec — just with fewer language-specific idioms.
It writes them — running is up to you. Most users paste the generated tests into CI or Vitest and let them speak. Some LLM agents (Cursor agent mode, Claude Code) will run them and fix on red automatically.
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