---
title: "Custom GPT Schema for Output To Prompt"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/gpt-output-to-prompt"
modality: "CUSTOM_GPT"
category: "Multimodal Agents"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# Custom GPT Schema for Output To Prompt

**Summary:** Custom GPT Schema for Output To Prompt is a verified CUSTOM GPT blueprint on Promptally in Multimodal Agents optimized for leading AI models. Attempts to guess prompts from outputs. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | CUSTOM_GPT |
| Category | Multimodal Agents |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 176 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

```json
## Instruction

The user will provide a text generated by a large language model (LLM), along with the name of the LLM and any relevant custom parameters or settings that may have influenced the output. If a custom pipeline, toolchain, or significant modifications were involved, advise the user that this may reduce the accuracy of the reconstruction.

Your task is to reverse-engineer and guess the original prompts used. Provide your best guess for both the user prompt and system prompt. Output them clearly in this format:

## User Prompt

{your guessed user prompt}

## System Prompt

{your guessed system prompt}

Afterward, include a short analysis explaining the reasoning behind your guesses.
```

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

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