Quality Assurance

Introduction

Quality Assurance for prompts blends evals, telemetry, human review, and ongoing regression testing. It's how prompts stay great after launch.

Beginner analogy: Like restaurant QA — kitchen tests, taste tests, customer feedback, all running together.

Understanding the topic

Core concepts to understand:

• Pre-launch: evals + adversarial tests + human review.
• Live: sample N% of traffic, score with LLM-judge + human.
• Weekly: regression run, error analysis, prompt updates.
• Quarterly: full re-benchmark across all models.

Syntax reference

Visual workflow / architecture:

Pre-launch → CI evals
Launch → 5% sampled scoring
Weekly → regression + error analysis
Quarterly → full benchmark

Real-world use

Enterprise AI teams treat prompt QA like product QA — checklists, owners, dashboards.

Best practices

• Assign each prompt an owner.
• Dashboards visible to product, not just engineering.
• Track 'last reviewed' date per prompt.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What does prompt QA look like?
• Q2. How sample live traffic?
• Q3. Who owns prompts?
• Q4. Cadence of regression?