Evaluate JSON Output from LLMs
A prompt workflow example that demonstrates how to evaluate and validate JSON output from language models, ensuring structured responses meet expected schema
Why it matters
Ensure your Large Language Model outputs are consistently valid JSON. This asset helps you programmatically check and validate the structure and content of JSON responses.
Outcomes
What it gets done
Validate LLM-generated JSON against a schema.
Extract specific data points from JSON output.
Classify JSON output based on predefined criteria.
Automate the evaluation of LLM responses.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/pfoo-eval-json-output | bash Capabilities
What this chain does
Pulls structured data fields from unstructured text.
Labels or categorizes text, files, or data points.
Writes and executes SQL or NoSQL queries on databases.
Overview
Eval Json Output
What it does
This is a working example from the promptfoo framework that demonstrates JSON output evaluation. It provides a runnable workflow for testing whether language model responses conform to expected JSON schemas and structure requirements.
How it connects
Use this example when you need to implement quality checks for JSON-formatted AI responses, when building systems that depend on structured model output, or when learning how to set up automated validation for language model responses in the promptfoo framework.
Source README
eval-json-output (Json Output)
You can run this example with:
npx promptfoo@latest init --example eval-json-output
cd eval-json-output
Usage
To get started, set your OPENAI_API_KEY environment variable.
Next, edit promptfooconfig.yaml.
Then run:
promptfoo eval
Afterwards, you can view the results by running promptfoo view
Discussion
Questions & comments · 0
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