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The Prompt-to-Spec Converter
Turn a vague ask or a messy prompt into a rigorous, model-agnostic prompt spec — a role, typed {'{{inputs}}'}, an output contract with a JSON schema, guardrails, eval checks, and a recommended fallback chain.
# The Prompt-to-Spec Converter — a free tool by VernaOne (https://verna.one)
# Paste this whole prompt into ChatGPT, Claude, or Gemini, then fill in the task at the bottom.
You are a senior prompt engineer. Convert the rough task or messy prompt provided at the bottom into a **production-ready prompt spec**. Be rigorous and concrete; do not add capabilities the task didn't ask for.
Produce the spec in exactly these sections, in order:
## 1. Goal
One or two sentences: what the prompt must reliably do, and for whom.
## 2. Role / system instruction
The system prompt: the model's role, tone, and the hard rules it must follow. Keep it model-agnostic (no provider-specific syntax).
## 3. Inputs
A table of the templated inputs the prompt takes, one row each:
| Variable | Type | Required | Description |
Use `{{snake_case}}` names. Include only inputs the task actually needs.
## 4. Output contract
State the exact output format. If the output is structured, give a strict **JSON Schema** (types, required fields, enums) the response must satisfy. If it's prose, define the shape (sections, length limits). Be explicit enough that code could validate the result.
## 5. Guardrails
Refusal conditions, safety limits, what to do on missing/invalid input, and how to signal an error inside the output contract (don't break the schema).
## 6. Eval checks
3–6 automatic pass/fail checks that grade any output of this prompt. Prefer machine-checkable ones: `schema-match`, `contains`, `regex`, `length`, `is-valid-json`. For each, give the check type and the exact condition.
## 7. Recommended models & fallback chain
Suggest a primary model and an ordered, **cross-provider** fallback chain suited to this task's difficulty and cost sensitivity. One line on why. Note that the final prompt is model-agnostic, so switching is a config change.
## 8. The rewritten prompt
The final, ready-to-use prompt with the `{{variables}}` from section 3 inlined. It must be self-contained, model-agnostic, and consistent with the output contract above.
Rules:
- If the task is ambiguous, make the smallest reasonable assumption and note it in one line under "Assumptions" before section 1 — do not ask me questions.
- Keep it tight. No filler, no restating these instructions.
---
TASK OR PROMPT TO CONVERT:
{{PASTE YOUR ROUGH TASK OR EXISTING PROMPT HERE}}
↑ The header line (with the verna.one link) travels with the prompt when you share it.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, or Gemini.
- Paste your rough task or existing prompt at the bottom, where it says so.
- Get a full spec — role, typed inputs, output contract, evals, fallback chain, and the rewritten prompt.
- Ship it as a versioned endpoint in VernaOne with the inputs, contract, and evals attached.
Run it for real, not just once
VernaOne turns the result into a versioned, model-agnostic endpoint with automatic fallback across every provider — call one name, change models without changing code.
Try VernaOne free →Frequently asked
What is a prompt spec?
A prompt spec is the contract around a prompt: its goal, the role/system instruction, the typed inputs it accepts ({{variables}} with types and whether they're required), the exact output format (often a JSON schema), guardrails, and the checks that decide whether an output passes. Treating a prompt as a spec — not a loose string — is what makes it testable, versionable, and safe to run in production.
Why turn a prompt into a spec before shipping it?
A loose prompt has no defined inputs, no output contract, and no way to catch a bad response. Converting it to a spec forces those decisions up front: your code gets predictable structured output, bad inputs are caught before you pay for a call, and eval checks flag regressions automatically. It also makes the prompt model-agnostic, so you can switch or fall back across providers without a rewrite.
How do I run the resulting spec in production?
Create the rewritten prompt as a named, versioned endpoint in VernaOne, declare the typed inputs, attach the output contract and eval checks, and set the recommended fallback chain. Then call it by name from the SDK or HTTP API — VernaOne handles model selection, automatic fallback, and cost analytics, so the spec becomes a running, governed endpoint.
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