LLM Evaluation Pipeline

Overview

Built a pipeline to systematically evaluate LLM outputs for quality, consistency, and correctness.

Responsibilities

• Designed evaluation metrics
• Automated test case generation
• Compared prompt strategies

Approach

• Prompt A/B testing
• Output scoring
• Regression testing

Pipeline

flowchart LR
    A[Test Inputs] --> B[Prompt Variants]
    B --> C[LLM Outputs]
    C --> D[Evaluation Metrics]
    D --> E[Performance Report]

Tech

Python · OpenAI

Impact

• Improved reliability of LLM systems
• Enabled systematic prompt optimization
• Reduced production risks