Free tool · no signup
The Production Prompt Auditor
Paste any prompt from your codebase into ChatGPT or Claude and instantly see what will break when you switch models, where it wastes tokens, and get a model-agnostic rewrite you can ship.
- Cost: free, no account, no install.
- Works in: ChatGPT, Claude, Gemini, or any capable LLM.
- Output: portability score, cost flags, a model-agnostic rewrite, and a ship-it checklist.
- Made by: VernaOne — the prompt platform that turns prompts into versioned, model-agnostic endpoints.
# The Production Prompt Auditor
# A free tool from VernaOne — https://verna.one/prompt-auditor
# Paste this whole message into ChatGPT, Claude, Gemini, or any capable LLM,
# then paste your own prompt where marked. No signup, no install.
# ---------------------------------------------------------------------------
You are a senior AI engineer running a production-readiness audit on a prompt
that a developer is about to ship — or has already shipped — inside an
application. Your job is to find what will break when the model changes, what
costs too much, and what makes it fragile in production.
Audit the prompt under <prompt_to_audit>. Return your findings in exactly
these four sections, in Markdown. Be specific and terse. No preamble.
## 1. Portability & lock-in — Portability Score: X/100
List every part of the prompt that depends on one specific provider or model
and would silently break, degrade, or behave differently on another. Look for:
provider-specific system-message rules, OpenAI `response_format` / JSON mode,
function- or tool-call schemas, Anthropic-style <thinking>/XML tags,
reasoning-effort or `temperature` assumptions, hard-coded model or token
limits, and wording tuned to one model's quirks. For each: what breaks, and on
which models. Then give a Portability Score /100 (100 = runs identically on any
capable model).
## 2. Cost & token waste
Flag anything that spends tokens without buying quality: restated context,
verbose boilerplate, large raw JSON/data blobs pasted inline, few-shot examples
that could be trimmed, and any missing output-length ceiling. Estimate the
rough % of input tokens that are structural waste.
## 3. Model-agnostic rewrite
Rewrite the prompt to run cleanly on any capable model. Requirements:
- separate a clear **System** instruction from the **User** message;
- replace every inlined value with a named `{{variable}}` placeholder;
- state the output format explicitly, and prefer plain JSON over any
provider-specific mode;
- strip single-model quirks while keeping behavior identical.
Return it as one labelled code block.
## 4. Ship-it checklist
- **Fallback chain:** a primary + 2 fallback models from *different* providers
that should give equivalent results — cheapest-capable first.
- **Output contract:** the exact format/schema to validate every response.
- **Evals:** 2–3 concrete pass/fail checks to run on each response.
If something is already solid, say so in one line and move on.
<prompt_to_audit>
{{PASTE YOUR PROMPT HERE}}
</prompt_to_audit>
↑ The header line travels with the prompt when you share it. Send it to a teammate who ships prompts.
How to use it
- Copy the auditor prompt above.
- Paste it into ChatGPT, Claude, or Gemini.
- Replace
{{PASTE YOUR PROMPT HERE}}with a prompt you actually use in code. - Read the audit — portability score, token-waste flags, a model-agnostic rewrite, and a fallback model chain.
Why model-agnostic prompts matter
Most prompts are quietly welded to the model they were written for. A prompt tuned around OpenAI's JSON mode, Anthropic's XML tags, or a specific model's phrasing will break or silently degrade the day you try to switch — for cost, latency, availability, or a better model shipping next week. That's provider lock-in, and you usually discover it at the worst possible time.
A portable prompt separates instruction from data, templatizes its inputs, pins an explicit output contract, and avoids single-vendor constructs. Then swapping GPT-4o for Claude, Gemini, or a cheaper open model is a config change, not a rewrite.
| Locked-in prompt | Model-agnostic prompt |
|---|---|
| Relies on one provider's JSON mode / tool schema | States output format as plain JSON any model returns |
| Data pasted inline, mixed with instructions | Inputs templated as {{variables}}, system/user split |
| No fallback — one outage = downtime | Primary + fallback models across providers |
| Switching models = rewrite & re-test | Switching models = change one line |
Ship the audited prompt as a real endpoint
The auditor tells you what to fix. VernaOne makes it permanent: paste the rewritten
prompt in and it becomes a versioned, model-agnostic endpoint with automatic
fallback, side-by-side model comparison, cost analytics, and lossless token compression. Call
one name; change models without changing code.
Try VernaOne free → Frequently asked
How do I make my LLM prompts work across different providers (model-agnostic)?
Remove anything tied to one provider — OpenAI response_format/JSON mode, Anthropic-style XML or <thinking> tags, function-call schemas, reasoning-effort and temperature assumptions, and hard-coded token limits — then separate a clear system instruction from the user message, templatize every inlined value as a {{variable}}, and state the output format as plain JSON. The free Production Prompt Auditor does this automatically and scores your prompt's portability. VernaOne then runs that prompt as a single versioned endpoint across providers with automatic fallback, so switching models needs no code change.
What is prompt provider lock-in?
Provider lock-in is when a prompt only works well on the model it was written for. Provider-specific constructs (JSON mode, tool-call schemas, XML tags, model-specific phrasing) make the prompt break or degrade silently on any other model, so you can't switch providers for cost, latency, or availability without rewriting.
How do I reduce token cost on a prompt?
Cut structural waste: restated context, verbose boilerplate instructions, large raw JSON blobs pasted inline, over-long few-shot examples, and missing output-length ceilings. Move the stable instruction prefix into a provider cache, and losslessly compact large JSON inputs. VernaOne applies lossless columnar JSON compression and output shaping automatically, typically cutting 28–49% of the tokens on data-heavy prompts.
What is the best way to manage prompts in production?
Treat prompts like versioned code, not strings in your source: give each prompt a stable name and version, template its inputs, pin an output contract, define fallback models across providers, and track cost and quality per version. VernaOne is a prompt registry and multi-provider router that does this.
Is the Production Prompt Auditor free?
Yes. It is a free copy-paste prompt with no signup and no install. Paste it into any LLM along with your own prompt and read the audit.
Found this useful? The fastest thanks is sending the auditor prompt to one engineer who ships prompts.