Collect
Start with requirements, test cases, bug reports or API documentation. Your QA knowledge is the source.
stories.md · defects.csv
LIVE WEBINAR
AI can write test cases.
But does it know your product?
Generic AI hasn't read your user stories, API rules or past defects. Discover how RAG finds the right project context before AI answers—so you can check test ideas against real evidence.
A practical QA-first walkthrough
No machine learning background needed · Easy to follow
✓ Real QA scenarios✓ Source-backed answers✓ Beginner-friendly
Does any of this sound like your week?AI-generated tests look good.
AI-generated tests look good.
Then the real QA work begins.
Choose the situations you've faced. These aren't prompting problems alone—they're often missing-context problems.
Recognise any of these? Keep reading to see what's missing.
There is a common thread.
See why it happens General AI can help draft tests. But it doesn't automatically know your business rules, past defects or edge cases.
The problem isn't simply a bad promptWhy generic AI
Why generic AI
fails at testing.
AI understands common testing patterns. It doesn't know what makes your product different.
It hasn't seen your user stories, API contracts or defect history unless those details are provided. So it can fill the gaps with a convincing answer that misses the rule your team actually tests.
01
Invented features
Tests for a button or flow that isn't in your application.
02
Missed edge cases
Good-looking happy paths, but no critical boundary or past regression.
03
No reliable trail
Nothing linking the generated step to a requirement you can validate.
Try it: select a QA question, compare both answers, and inspect the example source.
Your project. Connected.Interactive preview
Pick a QA question
Write test cases for the refund approval limit.
Illustrative unsupported output
Example generic output: refund $500 using a Wallet button, then verify an instant refund. The currency, button and approval behaviour are not supported by the sample documents.
↳ Answer using retrieved context
Test ₹9,999: no manager approval.
Test ₹10,000: manager approval required; regression for DEF-1193.
Test ₹10,001: manager approval required.
REQ-482 + DEF-1193 · View evidence ↗
REQ-482: Refunds of ₹10,000 or more require manager approval. DEF-1193: A refund of exactly ₹10,000 previously skipped approval; fixed in v4.2.
Find context → Generate → VerifySource included ✓
Tap the comparison. Static illustrative answers, not a live AI service.
WHAT RAG CHANGES
Try the comparison ↗Before answering, retrieve the relevant requirement and defect. Then let the AI use that context—and have a tester verify the source.
How RAG actually worksRAG in four steps.
RAG in four steps.
No data science degree required.
Turn scattered QA documents into context an AI can use—and answers a tester can verify.
Index
Split documents into smaller passages and make them searchable—often with embeddings and a vector database.
Chunk → embed → index
Retrieve
Ask a question. The system finds relevant passages, instead of expecting the AI to know your product by default.
Find the matching rule
Generate & verify
AI answers using retrieved context. You open the cited source and test whether the claim actually holds.
Answer + source check
You don't need to learn the mathematics behind embeddings to understand the workflow. You do need to know what evidence to trust.
FROM PROMPTS TO PROJECT CONTEXT
Reserve my seat See how a QA question becomes a source-backed answer.
A practical 90-minute live walkthrough of RAG for testers.
Inside the 90-minute live webinarWhat you'll actually see,
What you'll actually see,
step by step.
One QA-focused walkthrough: why generic AI misses context, how RAG finds it and how a tester checks the answer.
- 01 · Recognise
Why GenAI alone misses product rules
Understand hallucinations, missing requirements and confident guesses using real-world-style QA examples.
- 02 · Prepare
Turn documents into searchable knowledge
See requirements ingested, broken into chunks and indexed so the right passage can be found.
- 03 · Retrieve
Ask a question grounded in your project
Follow a QA question as relevant passages are retrieved and given to the language model.
- 04 · Verify
Check generated test ideas against the source
Compare answers, inspect citations and look for missing conditions or boundary cases.
A guided demonstration with sample QA data—not a promise of a production-ready system or direct integrations with every QA tool.
Your QA experience still mattersRAG brings the context.
RAG brings the context.
You bring the judgment.
A source citation isn't automatically proof that an AI answer is correct. You still verify the rule, the boundary and the evidence. That's exactly why a tester's mindset matters.
Better context is a starting point—not a replacement for testing.
Designed for QA, not data scientistsIf you test software,
If you test software,
this is for you.
See familiar testing challenges explained through the lens of project-aware AI.
Manual testers
Connect requirements, edge cases and bug history to how AI generates test ideas.
Automation testers & SDETs
Understand how retrieved context can support better-informed test design and automation planning.
QA leads & managers
See what a team should verify before using AI output in a real QA workflow.
What you'll take awayA practical RAG mindset
A practical RAG mindset
for your QA team.
Not another list of AI buzzwords. A workflow you can explain, question and evaluate with your team's requirements.
Explore the live webinar →- Explain RAG without the jargonKnow why project-specific AI needs collection, indexing, retrieval and source checking.
- Recognise useful QA applicationsRequirement Q&A, defect-history search and drafting test ideas with the right context.
- Ask the right trust questionsWhich requirement was retrieved? Did it support the answer? Which boundary cases are still missing?
- Identify a realistic next stepKnow what to investigate before trying RAG on your own testing documents.
Your trainer
Prabakaran Murugan
Senior AI Trainer
Testleaf Software Solutions Private Limited
Testleaf Software Solutions Private Limited
A practical session connecting QA, test automation and AI workflows. Follow the application step by step, with explanations designed for testing professionals.
Your next step in AI-assisted QAStop guessing the rules.
Stop guessing the rules.
Start finding the evidence.
See how requirements and defect history can give AI answers the context a QA professional actually needs. One focused 90-minute live session.
Reserve my seatContinue to Testleaf registration.
Confirm session details before booking.
Confirm session details before booking.
Before you register
Questions QA professionals ask.
Do I need to know Python, machine learning or how to code?
No. The webinar starts with a QA-friendly explanation of RAG. No coding is needed to follow the live demonstration. This is a guided session, not a coding bootcamp.
Is this useful for manual testers?
Yes. The examples start with the work you already know—requirements, boundary cases, test cases and historical defects. You don't need an automation background to understand them.
How is this different from a general GenAI webinar?
Instead of broad prompting tips, this session focuses on one testing problem: helping AI find relevant project context before it answers, then checking whether the cited evidence really supports the output.
Will I build a complete RAG application in 90 minutes?
You'll follow a live demonstration from document ingestion through retrieval and source-backed answers. It is not a full implementation course, and a ready-to-deploy production system is not promised.
Will RAG work with Jira, Confluence, Selenium or Playwright?
The concepts are relevant to QA information stored across these kinds of tools. The demonstration uses sample documents; it does not promise a live integration with every platform or a plug-and-play connector.
When is the webinar, and what is the fee?
The session date and time are yet to be announced. The fee has not been specified on this page. Please confirm these details on the registration page before booking.
What should I prepare?
A laptop or desktop is recommended for a clear view of the demo. No project files or prior RAG setup are required to follow along.