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AI Testing Notes Downloads

This page lists Uncodemy's AI Testing notes, each as its own file. AI testing here means checking AI-based applications such as chatbots and retrieval-augmented generation (RAG) systems for accuracy, safety and consistency. Because outputs vary, testers use evaluation sets, metrics and human review instead of one fixed expected answer.

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Prepared by: Uncodemy trainer team · Reviewed by: Uncodemy reviewer · Last reviewed: 24 Oct 2026

Downloads

Download AI Testing Notes

Each resource is a separate file. Choose the one that matches what you need.

Set 1

AI Testing Notes — Core Set

Covers the basics of testing LLM applications, including accuracy, hallucination and consistency.

Updated 24 Oct 2026

Download Set 1

Set 2

AI Testing Notes — Advanced Set

Focuses on RAG systems, safety, security and performance metrics for AI applications.

Updated 24 Oct 2026

Download Set 2

Set 3

AI Testing Notes — Practice Set

Practice-oriented notes with evaluation techniques, common mistakes to avoid and a starter checklist.

Updated 24 Oct 2026

Download Set 3

Clarity

Two meanings of AI testing

Understanding the difference is crucial for the right approach.

The core difference

AI testing can mean using AI tools to help write or run tests, or testing an AI-based application itself. These notes focus on the second: testing LLM applications, chatbots and RAG systems.

The key difference from normal testing is that the same input can give different outputs, so a single expected string is rarely enough.

Coverage

What to test in an LLM application

Key areas to focus on when testing LLM-based systems.

Area	Question	Example check
Accuracy	Is the answer correct and supported?	Compare with a reference answer or source document
Hallucination	Does it invent facts?	Questions with no answer in the documents; expect a refusal
Retrieval (RAG)	Did it fetch the right passages?	Check retrieved chunks against known relevant ones
Safety and security	Can it be tricked or leak data?	Prompt-injection and sensitive-data tests
Consistency	Are answers stable across runs?	Repeat the same prompts and compare
Bias and tone	Is it fair and appropriate?	Review sample outputs across groups and topics
Performance	Is it fast and affordable?	Measure latency and token cost

Toolkit

Techniques & Common Mistakes

Best practices and pitfalls to watch out for.

Techniques testers use

A fixed evaluation set of questions with reference answers.
Metrics for retrieval and answer quality, using evaluation tools such as RAGAS where suitable.
Tracing and logging of runs, for example with LangSmith or similar tools.
Human review of a sample, since automated scores are imperfect.
Regression runs after every prompt or model change.

Common mistakes

Testing with only a few prompts.
Expecting exact-match outputs.
Relying only on automated scores without human review.
Ignoring security tests such as prompt injection.

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About this study material

This material is for learning and revision. It is not a certificate, a degree or a promise of a job, and technology changes, so check official documentation for the latest details. Spot an error? Write to info@uncodemy.com and the trainer team will review it.

FAQ

Frequently Asked Questions

What is AI testing?

AI testing usually means checking AI-based applications such as chatbots and RAG systems for accuracy, safety and consistency. It can also mean using AI tools to help with testing.

How is testing an LLM application different from normal testing?

LLM outputs vary between runs, so testers use evaluation sets, metrics and human review instead of a single exact expected output.

What is hallucination testing?

Hallucination testing checks whether a model invents facts. A common method is to ask questions that the supplied documents cannot answer and check that the system declines to guess.

What is RAG testing?

RAG testing checks both parts of a retrieval-augmented system: whether the right passages are retrieved and whether the answer is grounded in them.

Can testers move into AI testing?

Yes. Testing fundamentals, API testing and some Python are a good base, and AI-specific methods such as evaluation sets can be learned on top.

Are these resources free to download?

The download cards on this page link directly to the files. Use the button on the card that matches what you need.

AI Testing Notes Downloads

Download AI Testing Notes

AI Testing Notes — Core Set

AI Testing Notes — Advanced Set

AI Testing Notes — Practice Set

Two meanings of AI testing

The core difference

What to test in an LLM application

Techniques & Common Mistakes

Techniques testers use

Common mistakes

Related Uncodemy resources

Learn with a trainer

About this study material

Frequently Asked Questions