LLM Output Validator (Mock Mode)

A lightweight Python CLI tool that validates AI-generated responses using configurable rule-based guardrails and produces structured JSON reports.

Why This Project Matters

This project demonstrates:

• Practical AI workflow validation
• Rule-based output guardrails
• Input normalization and scoring
• Structured JSON reporting
• Clean separation of configuration and logic
• Portfolio-ready CLI tooling

This simulates how AI outputs can be programmatically evaluated in production pipelines.

Example

Input: "This response failed due to an exception."

Output: FAILED Score: 80 Issues:

• flag_words: Found 'exception'

Real-World Application

This tool demonstrates how rule-based validation can be used in AI-assisted workflows, such as:

• Automated LLM output quality assurance
• Guardrail enforcement in production pipelines
• Prompt response scoring systems
• Compliance and safety filtering
• Structured evaluation before database ingestion

While this project runs in mock mode, the architecture supports integration with live LLM APIs.