Who wants free courses? 👀 Help us pick the topics!

1.AI will never replace tester

2.How is AI helpful for current era in software testing

3.How to reduce hallucination to get better output

Basics of LLM and use of it in Test automation 

1. AI evaluation framework

2. Build AI subagents

Can AI/Gen AI be used by manual testers or those with little automation knowledge? 

• Introduction to Large Language Models (LLMs)

• Prompt Engineering & Fine-Tuning

• AI in Software Testing 

AI to ensure Quality

Prompt Engineering in Testing

Quality in the Age of AI

Humans vs. Machines: Testing in the AI Era

The Tester’s Guide to AI

From Bugs to AI Bots

• Introduction to Small Language Models (SLMs)
• How to create MCP for SLM?
• Beyond Prompt Engineering 

Hi,

First of all thanks for this initiative.

Can you please create a course on what AI tools should QA learn? Like learn about creating AI agents and prompt engineering. I am not clear on what to learn and in which order.

Also please create the videos of each course you suggest.

Thanks again.

Lets have a course covering:

1. Introduction to AI, Local LLMs, Agents

2. Building Test Automation Framework with the help of AI

3. Building generic Test Automation Framework (single framework for Web, Mobile (both android and ios), and API testing), developing reports and sending it via mail.

4. Maintaining the framework, Self healing code

5. Deploying framework to CI/CD

6. Other reportings tasks using AI for QA Leadership.

​@PolinaKr ,

• AI-Powered Test Automation – Using tools like TestDriver & Playwright AI to automate smarter.
• Machine Learning Model Testing – Validating AI/ML systems for accuracy & reliability.
• AI in CI/CD & DevOps – Integrating AI to catch bugs early in pipelines.
• AI-Driven Performance & Load Testing – Predicting bottlenecks before they hit production.
• Ethical & Responsible AI in QA – Ensuring AI models & automation are safe and bias-free.

Can’t wait to see these in action! 

1. Courses on Critical thinking 

2. How to be a 10x productive tester

3. Course of Rapid Software Testing

4. How not to use GenAI in testing.

Flutter App automation framework that can help to Test release build and get good report on email.

Doing QA for AI Application

AI and its use in Automation testing.Also,how have you integrated AI into your products!

What career paths are there for Test Managers/Test Leads who are not individual contributors but are catalysts for AI adoption and implementation in organisations?

Automation of manual testing (not AT, but more of how tu use AI or Automation for more edge cases and for teams that they can not be full AT yet) 

how to create agent for automation testing

how to create mcp server for test automation

prompt engineering

Ollama, DeepSeek, Deep Eval for Testing, RAG

API testing using postman and restassured

Course on Skill Decay in Testers which focuses on:

• How over-automation reduces exploratory ability
• How AI reduces system understanding
• How testers unknowingly deskill themselves

1: From Test Execution - System
Validation
Shift mindset first.
Learn:
Tokens, embeddings, temperature
Determinism vs variability
Why traditional testing breaks for GenAl
Core failure modes: hallucination, bias, prompt
sensitivity, latency & cost instability
Hands-on:
Build an LLM prompt-response evaluator
Compare outputs across temperature changes
Log prompts, responses & metadata
 2 - LLM Evaluation & Metrics (The Core
Upgrade)
GenAl quality * pass/fail.
Learn evaluation dimensions:
Correctness
Faithfulness
Relevance
Context recall
Accuracy vs usefulness
Hands-on:
Build automated evaluation pipelines
Run batch prompt experiments
Compare model versions objectively
Tools:
RAGAS . DeepEval . Braintrust
Deliverable: Your first LLM evaluation report
3 -- RAG & Knowledge Reliability Testing
Most hallucinations are retrieval failures, not model
failures.
Learn:
RAG architecture & failure points
Chunking issues
Embedding mismatch
Retrieval drift
Hands-on:

Test retrieval precision & recall
Inject corrupted documents
Validate answer faithfulness to sources
A now validates data pipelines, not just app logic
4 - Observability & Production Readiness
Logs are not enough for LLMs.
Learn:
Prompt lineage & versioning
Behavior drift detection
Tracing prompt tool - response chains
Hands-on:
Detect latency spikes & token explosions
Compare behavior across deployments
Tools:
LangSmith . Arize
Deliverable: Production-ready GenAl observability
dashboard
5-- Safety, Guardrails & Risk-Based Testing
This is where GenAl becomes enterprise critical.
Learn:
Data leakage risks
Unsafe instructions
Compliance & policy testing
Prompt fixes vs system controls
Hands-on:
Build red-team prompt suites
Validate refusal behavior
Test boundary violations
Tools:
Guardrails Al + policy-as-code checks

 6 - Testing Al Agents (The Future of QA)
Agents plan, act, fail, retry.. and surprise you.
Learn:
Agent architectures (planner, executor, memory)
Non-deterministic workflows
Why step-based test cases fail
Hands-on:
Test multi-step agents for:
Goal completion rate
Unsafe action rate
Recovery from failure
Human-in-the-loop gates