HotPrompt
Code·ChatGPT·Claude

Code prompt generator — turn one-liners into shippable specs

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

Before vs after — a code prompt your LLM can actually compile

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.

Before · Lazy promptLikely output: half-broken React

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
After · HotPromptReady for ChatGPT / Claude
# 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

How to write a code prompt LLMs can actually compile

Four steps to turn "build me a thing" into a spec ChatGPT or Claude can execute on the first try.

  1. 01

    Name the language

    TypeScript, Rust, Python — the generator picks idiomatic style, library defaults and a sensible runtime.

  2. 02

    List the edge cases

    Empty input, races, unicode, retries. They go on the contract so the LLM can't miss them.

  3. 03

    Ask for tests

    At minimum a happy-path and one failure mode. Tests turn vague specs into verifiable ones.

  4. 04

    Want a usage example

    Copy-pasteable, runnable, with sample input/output — the difference between docs and demos.

What you get from this AI code prompt generator

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.

Built for every modern coding assistant

Specs look slightly different depending on who's reading them. The generator adapts to the assistant you pick.

Cursor

IDE

Rich, multi-file context. The generator emits specs that reference imports, paths and file structure when relevant.

GitHub Copilot

Inline

Tighter prompts that fit Copilot's slim context window — the structural skeleton without prose.

Claude Code

Anthropic

XML-friendly section headers and long-context tolerance — Claude responds best to clearly labeled blocks.

ChatGPT / o1

OpenAI

Reasoning-friendly specs with explicit constraints and acceptance criteria for the o-series models.

DeepSeek Coder

DeepSeek

Compact but complete — DeepSeek's strength is the speed/quality balance on bounded specs.

Cody / Continue

Open IDE

Repo-aware specs. The generator stays surgical and avoids over-stating context the IDE already has.

Languages this prompt generator tunes for

Idioms and library defaults vary across stacks. The generator picks the right ones from your one-line idea.

TypeScript + React

Discriminated unions, props with sensible defaults, Vitest + Testing-Library tests.

Python + FastAPI

Type hints, Pydantic models, pytest fixtures and a runnable curl example.

Rust

Result<T, E> over panics, derive(Debug, Clone), property-based tests where they help.

Go

Errors as values, no panic-in-library, table-driven tests in a *_test.go file.

Swift + SwiftUI

ObservableObject, Combine where appropriate, XCTest cases with async/await.

SQL

Schema + migration + query + CTE explanation. Postgres-first, dialect noted.

Where this code prompt generator earns its keep

Anywhere an LLM is the bottleneck between an idea and a shipping commit.

Solo developers

Turn the AI from junior to senior with one click — every prompt arrives with tests already asked for.

Indie hackers

Ship MVPs in weekends. The spec is the design doc; the LLM is the dev team; you're the PM.

Engineering teams

Specs that both LLMs and humans parse — the prompt becomes the design doc on review.

Educators

Project briefs that grade themselves: contract + tests means students can self-verify.

Questions

Frequently asked about this tool

What's the difference between a 'code prompt' and just asking an LLM for code?

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.

Does this work with Cursor's @-mentions and file context?

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.

Can I use this with Claude Code or ChatGPT directly?

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.

What about non-React or non-Python stacks?

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.

Will the LLM actually run my tests?

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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