A complete guide to AI/LLM testing. A practical guide to testing AI and LLM-powered systems, written from real production experience. Covers hallucinations, prompt drift, RAG failures, and agent reliability, with frameworks and metrics tailored for SDETs and QA engineers transitioning from traditional automation to AI testing.
This repository contains a complete, experience-driven guide to testing AI, LLMs, RAG systems, and autonomous agents in real production environments.
It is written for engineers who already know how to test software and now need to test probabilistic systems where expected vs actual no longer applies, outputs vary by design, and failures emerge gradually rather than instantly.
This guide does not focus on model training or data science theory. It focuses on validation, reliability, and quality control for AI-powered systems after they are deployed.
This guide is designed for: SDETs and QA automation engineers Test architects and quality leads Backend and platform engineers responsible for AI reliability Teams testing LLM-powered features, RAG pipelines, or AI agents
If you have experience with Selenium, Playwright, Cypress, API testing, or CI pipelines, you already have the foundation needed. This guide shows how to translate those skills into AI testing.
By the end of this guide, you will be able to: Identify and test the six most common AI failure categories Replace brittle assertions with multi-oracle evaluation strategies Design tests for hallucination, prompt drift, over-refusal, and variance Validate RAG systems across retrieval, grounding, and answer correctness Test AI agents using trace validation, tool mocking, and time-bound execution Build an end-to-end AI evaluation pipeline integrated into CI/CD Define governance artifacts that QA teams should own for AI systems
This guide is structured to be read top-down, but each section is modular. Start with the mental model shifts required for AI testing Learn the core concepts only as deep as testing requires Apply structured frameworks for LLMs, RAG, and agents Use the practical exercises and examples to validate understanding Finish with the capstone project to build your own AI testing framework You can also use individual sections as reference material when designing or reviewing AI test strategies.
To complement execution practices with structured decision-making, ownership boundaries, and operational governance, refer to:
QA → AI Decision & Ownership Roadmap by Daria Tsion QA-AI-Decision-and-Ownership-Roadmap
The roadmap serves as an interactive governance layer addressing autonomy, human oversight, risk management, and QA accountability in AI systems.
The guide concludes with a capstone project where you design and evaluate an AI testing framework that incorporates:
Multiple oracle strategies Offline and live evaluation RAG and agent validation Metrics that reflect real system confidence
We welcome suggestions, corrections, and new ideas.
To share feedback:
Click on the Issues tab Select New Issue Choose “Roadmap Feedback” Describe your suggestion All constructive input is appreciated.
If you reference this roadmap, please cite:
Tanvi Mittal, "AI-Powered Testing Roadmap", GitHub, 2026