Testing and QA Training in Pune — Manual Testing, Selenium Automation and AI Testing

Which testing track should you take?

Three tracks. Foundation for people new to testing, Automation for testers moving into code, and AI Testing for experienced testers moving into evaluation. Most learners take two courses across two tracks, not all eight.

The one decision that trips people up is the Selenium language choice, so it is worth being explicit: Selenium with Java and Selenium with Python are alternatives, not a sequence. They teach the same automation concepts in different languages. Take Java if you are targeting services majors and enterprise automation, where the installed base is Java. Take Python if you want one language covering UI automation, API testing and — later — AI evaluation, because the AI testing courses are all Python.

The second decision is which AI course, and the boundary is sharper than the names suggest. AI-Assisted Software Testing uses AI to test ordinary software: it is one month, needs no Python, and suits any working tester. LLM & RAG Testing tests software built on AI, and needs Python and testing fundamentals. Agentic AI Testing tests systems that take real actions, and needs the LLM course or equivalent experience first.

How do the eight testing courses compare?

By coding requirement, main focus and who each one suits. Read the coding column first — it eliminates more wrong choices than anything else on this page.

• Software Testing & QA — no coding required. Manual QA fundamentals, ISTQB-aligned. Best for beginners and career switchers.
• Selenium with Java — coding required. Enterprise UI automation with TestNG and Maven. Best for the Java and services-hiring path.
• Selenium with Python — coding required. UI automation with pytest. Best for the Python path and anyone continuing into AI testing.
• Playwright with TypeScript — coding required. Modern UI and API automation, tracing and network mocking. Best for automation engineers modernising.
• API Testing & Automation — coding helpful. The service layer: REST, authentication, contract testing and schema validation. Best for testers going below the UI.
• AI-Assisted Software Testing — coding optional. Using AI across the QA workflow. Best for any experienced tester, in one month.
• Generative AI, LLM & RAG Testing — Python required. Evaluating AI applications: hallucination, grounding, RAG quality and safety. Best for AI QA specialists.
• Agentic AI Testing — Python and prior evaluation experience required. Testing agents that call tools and take actions. Best for senior QA moving into AI quality engineering.

Which course should you choose, given where you are now?

Answered by starting point rather than by course, because that is the question people actually arrive with.

• Completely new to testing — start with Software Testing & QA.
• You already know manual testing — choose Selenium with Java or Selenium with Python, not both.
• You already know Selenium — move to Playwright & API automation, or straight to AI-Assisted Software Testing if your automation is already solid.
• You are an experienced automation tester — start with AI-Assisted Software Testing, then LLM & RAG Testing.
• You already test GenAI applications — start with LLM & RAG Testing, or go to Agentic AI Testing after an assessment.
• You have 10 to 15 years of manual QA and no automation — Selenium with Python, then Playwright & API, then the AI track. Do not repeat beginner manual modules unless an assessment shows a genuine gap.
• Your company just shipped an AI feature and asked you to test it — LLM & RAG Testing, and bring the feature with you.

How do you get from manual testing to automation?

In order: testing fundamentals, then manual testing practice, then SQL, then enough programming to write test code, then Selenium, then a framework, then running it all in CI. Skipping to Selenium without the ground underneath it is the most common way learners stall.

The stage that surprises people is programming. Automation is software development — your test suite is an application, and it has to be structured, debugged and maintained like one. That is why the Selenium courses spend their opening weeks on Java or Python aimed squarely at testers rather than assuming you will pick it up alongside WebDriver.

The stage people skip is SQL, and it costs them in interviews. Verifying that the application stored what it displayed is a daily task, and a tester who cannot query the database is trusting the interface to tell the truth about itself.

Why does testing an AI feature need its own course?

Because the assertion stops working. Every testing skill you have assumes that the same input produces the same output — an LLM answers differently every time, and both answers may be correct. Exact-match assertions produce a suite that fails constantly while telling you nothing.

What replaces assertion is evaluation, and it has its own vocabulary: quality dimensions instead of pass and fail, rubrics instead of expected values, golden datasets instead of test cases, validated judges instead of comparisons, and statistical baselines instead of single runs. None of it is exotic, and none of it appears in a testing syllabus written before 2023.

Agents raise the stakes again. An LLM that produces a wrong sentence has given a bad answer; an agent that calls the right tool with plausible but wrong arguments has issued a refund, cancelled a booking or deleted a record. Agent testing has to judge the path taken as well as the final result, which is a form of assertion no traditional QA course teaches.

The commercial driver is straightforward: companies across Pune shipped AI features quickly and are now being asked — often by a customer's security review rather than by their own QA team — how they know those features are correct, safe and not leaking data. Very few testers can answer that, which is why this is currently the thinnest-staffed skill in the category.

• Non-determinism — the same prompt gives different valid answers
• Quality dimensions replace pass and fail: groundedness, faithfulness, refusal correctness
• Golden datasets and rubrics replace expected values
• LLM-as-a-Judge, and validating the judge before you trust it
• RAG splits the problem in two — retrieval quality and answer faithfulness
• Prompt injection and PII leakage are functional defects, not policy questions
• Agents add tool calls, trajectories, memory and irreversible actions

What does a software testing course cover?

The discipline before the tools — how software fails, how to design tests that find those failures, how to report a defect so a developer can act on it, and how testing fits into a release. Tools are taught afterwards, because a tool used without that judgement automates the wrong checks.

• SDLC and STLC — where testing sits in a release
• Test case design — equivalence partitioning, boundary values, decision tables
• Functional, regression, smoke, sanity and exploratory testing
• Defect lifecycle, severity versus priority, and writing a report worth acting on
• Test plans, traceability and coverage
• SQL for verifying results and preparing test data
• Agile and Scrum — testing inside a sprint
• Test management and defect-tracking tools
• API testing fundamentals with Postman
• ISTQB Foundation preparation, and an introduction to AI-assisted QA

What do QA interviews actually ask?

Two rounds, in effect. One on testing judgement — how would you test this, what would you check first, what is severity versus priority. One on automation — write a locator, explain a wait, describe your framework and why it is structured that way.

The automation half is where candidates are separated, and almost always on the same question: describe the framework you built. A candidate who used someone else's framework can name its parts; a candidate who built one can explain why the Page Object Model is worth the extra files, how they handled flaky waits, and what they would change. That is why the courses are structured around building one rather than demonstrating one.

A third question has appeared in the last two years and now comes up unprompted: how do you use AI in your testing work. A vague answer marks you as a non-adopter; "I generate test cases with ChatGPT" marks you as an uncritical one. The answer that lands describes what you generate, what you check it against, and something specific the AI got wrong that you caught.

• "How would you test this feature?" — asked about something ordinary, on the spot
• Severity versus priority, with an example of high one and low the other
• Writing an XPath or CSS locator for an element you are shown
• Implicit, explicit and fluent waits — and why Thread.sleep is the wrong answer
• Your framework: structure, reporting, data handling, and why
• A SQL query to verify what the application claims it saved
• Handling a test that fails intermittently
• Where your suite runs, and what happens when it fails in CI
• How you use AI in your testing work — and what you caught it getting wrong

Who should learn software testing?

Freshers and non-IT graduates looking for a realistic entry into the Pune IT industry, manual testers who need automation to stay employable, and support or operations staff moving toward an engineering role.

Testing remains the most forgiving entry point into IT for candidates without a computer-science degree, because the first months reward carefulness and systematic thinking rather than programming background. That advantage is real but time-limited: the same candidate needs automation within a year or two to keep moving, which is why treating manual testing as the destination rather than the doorway is the mistake to avoid.

• Freshers and graduates from any discipline seeking an IT entry point
• Non-IT graduates changing field into software
• Manual testers adding automation to stay competitive
• Support engineers and operations staff moving into engineering
• Automation engineers and SDETs moving into AI quality engineering
• Developers who want to understand testing properly
• Working professionals returning to IT after a break

Where does a testing career lead?

QA Engineer to Automation Engineer to SDET, then into test architecture, QA leadership or — newest and thinnest-staffed — AI quality engineering. The SDET route in particular is a development role, building the framework rather than using it, and pays accordingly.

Testing also has unusually good lateral movement. Automation engineers move into DevOps, because running suites in pipelines is the same skill set. API testers move into backend development. QA leads move into product and delivery roles, because they have spent years understanding what the software is supposed to do. The common factor in all three is having gone past manual testing into code.

The AI quality route is the newest and is worth a caveat. It pays above the general automation band because supply is thin rather than because the work is harder than SDET work, and thin supply is not permanent. What makes it durable is that the underlying skill — defining what good looks like when there is no expected value, and proving it repeatedly — is the same skill whatever the models do next.