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GPT-5.5 Model Evaluation Workflow

Data AnalysisCozeUpdated 2026-04-24

Evaluate GPT-5.5 on a specified dataset and automatically generate a performance report to help teams quickly assess model behavior.

System Prompt
Run model evaluation for {model_name} on {dataset_path} using {metrics} and output a report in {report_format}.

Variable Dictionary (fill in your AI tool)

This section only explains placeholders. It is not an input form on this website. Copy the prompt, then replace variables in Coze / Dify / ChatGPT.

{model_name}

The model to evaluate, e.g., GPT-5.5

Filling hint: replace this with your real business context.

{dataset_path}

Local or cloud dataset path containing the data to evaluate

Filling hint: replace this with your real business context.

{metrics}

List of evaluation metrics, e.g., accuracy, precision, recall, f1

Filling hint: replace this with your real business context.

{report_format}

Output format for the report, e.g., markdown, json, html

Filling hint: replace this with your real business context.

Quick Variable Filler (Optional)

Fill variables below to generate a ready-to-run prompt in your browser.

{model_name}

The model to evaluate, e.g., GPT-5.5

{dataset_path}

Local or cloud dataset path containing the data to evaluate

{metrics}

List of evaluation metrics, e.g., accuracy, precision, recall, f1

{report_format}

Output format for the report, e.g., markdown, json, html

Generated Prompt Preview

Missing: 4
Run model evaluation for {model_name} on {dataset_path} using {metrics} and output a report in {report_format}.

How to Use This Template

Best for

Teams that need faster data analysis output with more stable prompt quality.

Problem it solves

Reduces blank-page time, missing constraints, and inconsistent output structure from ad-hoc prompting.

Steps

  1. Copy the template prompt.
  2. Paste it into your AI tool (Coze / Dify / ChatGPT).
  3. Replace placeholder variables using the dictionary above.
  4. Run and refine constraints based on output quality.

Not ideal when

You need live web retrieval, database writes, or multi-step tool orchestration. Use full workflow automation for that.

Success Case

Input:
dataset_path: /data/test_set.csv
Output:
accuracy: 92.3%, precision: 90.1%, recall: 88.7%, f1: 89.4%

Boundary Case

Input:
dataset_path: /data/nonexistent.csv
Fix:
Ensure dataset_path points to a valid file with read permissions

What to Try Next

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Workflow Steps

  1. 1. Load and preprocess {dataset_path}

  2. 2. Initialize {model_name} and set inference parameters

  3. 3. Perform batch inference on the dataset and collect predictions

  4. 4. Compute {metrics} and generate a statistics table

  5. 5. Output the evaluation report in {report_format}

Constraints

  • Dataset rows > 10,000 requires batched inference
  • Model parameters > 10B requires GPU acceleration

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