Generative AI, LLM & RAG Testing Training in Pune
Pune's trusted LLM & RAG Testing classes at the Archer Infotech institute, Kothrud — weekday, weekend and online batches with placement assistance.
Test software whose answers are different every time. LLM application architecture for testers, the quality dimensions that replace exact-match assertions, hallucination and grounding tests, golden datasets, LLM-as-a-Judge, RAG retrieval and faithfulness evaluation, prompt-injection and safety testing, and latency, token and cost measurement — automated in Python and run in CI.
4.9 from 24 Google reviews- Trained
- 10000+ Trained
- Placed
- 5000+ Placed
- Placement rate
- 90% Placement rate
- 17+ years
- Since 2009 17+ years
Curriculum last reviewed:
Reviewed by Yogesh Patil, Founder & Director
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What is the Generative AI, LLM & RAG Testing course in Pune?
In short — Built for working developers who already have production experience: a 2-month path from fundamentals to job-ready Generative AI, LLM & RAG Testing skills, taught at our Kothrud, Pune centre or live online, with placement assistance and real project work you can show an interviewer.
Generative AI, LLM & RAG Testing training at Archer Infotech is a 2-month advanced programme in Pune. It runs as classroom batches at the Kothrud centre and as live online batches, using the same curriculum and trainers. The syllabus covers 12 modules and includes hands-on project work. It prepares learners for roles such as GenAI Test Engineer, LLM QA Engineer and AI Evaluation Engineer. Archer Infotech has trained IT professionals in Pune since 2009 and reports a 90% placement rate across learners who complete training. Fees, batch dates and EMI options are shared on request; a free demo class is available before enrolling.
Our LLM & RAG Testing students get placed at
And many more — 100+ corporate partners hiring across Pune and India.
This two-month course teaches testers to evaluate software that answers differently every time it is asked. You move from deterministic assertions to evaluation: quality dimensions for LLM responses, hallucination and grounding tests, golden datasets and rubrics, LLM-as-a-Judge, semantic similarity, full RAG evaluation covering retrieval and faithfulness, prompt-injection and PII-leakage safety suites, and latency, token and cost measurement — all automated in Python with pytest and run as release gates in CI.
Why LLM and RAG Testing Is a Separate Discipline
Every testing skill you have assumes determinism. Given this input, expect that output; if they differ, fail. An LLM breaks the assumption at the root — ask the same question twice and you get two different sentences, both of which may be entirely correct. The reflex of writing an exact-match assertion produces a suite that fails constantly while telling you nothing, and this is the single most common way teams' first attempt at AI testing collapses.
What replaces it is evaluation, and it is a genuine discipline with its own vocabulary: quality dimensions rather than pass and fail, rubrics rather than expected values, golden datasets rather than test cases, judges — human and model — rather than assertions, and statistical baselines rather than single runs. None of it is exotic; all of it has to be learned deliberately, because none of it is in a testing syllabus written before 2023.
The commercial pressure is straightforward. Companies across Pune shipped AI features quickly and are now being asked how they know the features are correct, safe and not leaking data — often by a customer's security review rather than by their own QA team. Testers who can answer that are scarce, and the scarcity is why this specialisation pays above the general automation band. It is also why the course spends a full module on safety, which is the question that comes up first in those reviews.
- Evaluation replaces assertion — taught as the central shift
- Golden datasets, rubrics and regression baselines you build yourself
- LLM-as-a-Judge, including where judges are biased and wrong
- Full RAG evaluation — retrieval, grounding, citations, tenant isolation
- Prompt injection, PII and secret leakage, safety regression
- Latency, tokens and cost as first-class test metrics
- Tool-neutral: DeepEval, RAGAS, Promptfoo and LangSmith as examples only
Who should take this Generative AI, LLM & RAG Testing course?
What does the LLM & RAG Testing syllabus cover?
Download the full syllabus as a PDF
The complete twelve-module syllabus as a PDF — AI application architecture, quality dimensions, LLM test design, hallucination and grounding, golden datasets, evaluation methods including LLM-as-a-Judge, Python test automation, RAG fundamentals and evaluation, safety and prompt-injection testing, performance and cost, evaluation frameworks and the capstone. Everything in it is on this page; the PDF is the portable version.
The shift this course teaches
- Assertion becomes evaluation: no expected value exists, so you define quality dimensions and grade against them.
- Test cases become golden datasets, versioned and compared against a baseline rather than passed or failed once.
- Safety becomes functional testing — prompt injection, PII leakage and system-prompt disclosure are defects, not policy questions.
- Cost and latency become test metrics, because a fallback to a cheaper model is a silent quality change.
Roles this course prepares you for
What projects will you build?
What jobs and salaries follow this course in Pune?
This is a scarce skill with real demand behind it. Pune companies shipped AI features at speed between 2024 and 2026, and are now facing the consequence: customers' security reviews, regulators and their own release processes all ask how the AI behaviour is verified, and most QA teams have no method to offer. A tester who can build a golden dataset, run an evaluation suite and produce a defensible quality report is answering a question the organisation currently cannot.
The roles this leads to sit above the general automation band, because the supply is thin rather than because the work is harder than SDET work. GenAI Test Engineer, LLM QA Engineer, AI Evaluation Engineer and RAG Quality Engineer are all titles in current use, with the caveat that naming has not settled — the same job appears under four labels, and you should read the responsibilities rather than the title. Product companies and GCC captives hire for this well ahead of services firms.
The honest constraint is that this is an advanced course and the market treats it that way. It rewards testers who already have automation and API experience; a fresher with no testing background will not convert this into a job, and we would rather say so at enrolment than at graduation. Placement support is included; we do not guarantee placement, and the institute-records rate is 90% across all tracks.
| Role | Salary band | Source |
|---|---|---|
| QA Automation Engineer (1–3 yrs) — the base this builds on | ₹6–10 LPA | Indeed Pune QA Automation listings (last 12 mo) |
| SDET (3–6 yrs) | ₹10–18 LPA | Glassdoor Pune SDET |
| AI / GenAI engineering roles — Pune band for comparison | ₹8–22 LPA depending on experience | Archer Infotech placement-team data, last 12 months |
Pune companies hiring LLM & RAG Testing professionals in 2026
Roles after this LLM & RAG Testing course
How long is the course, and what batch options are there?
Duration: 2 months — 8 weeks of taught content, plus the capstone harness build
Maximum 15 per batch. New batches roughly every 6 weeks. Model API usage across the whole course costs a few hundred rupees at most on pay-as-you-go tiers; we show you how to keep it there.
What are the LLM & RAG Testing course fees in Pune?
What placement support do you get?
Placement support is included at no separate charge, and for this course the capstone harness does most of the work. It is an unusual artefact — very few candidates arrive with a working evaluation suite, a validated judge and a CI release gate — and it converts the interview from a discussion about whether you understand AI testing into a walkthrough of something you built.
We do not guarantee placement. The institute-records rate is 90% across all tracks, measured on learners who complete training and clear at least one mock-interview round. For this track the realistic pattern is an internal move into an AI quality role at your current employer as often as an external one, because the teams that need this skill usually already employ you.
How does Archer Infotech compare with other institutes?
Factual rows. Worth using as a checklist against any LLM testing course, including this one.
| Factor | Archer Infotech | Typical Pune institute |
|---|---|---|
| Vendor dependence | Concepts first; DeepEval, RAGAS, Promptfoo and LangSmith shown as examples of shared patterns | Built around one framework's API, and dated within a year |
| LLM-as-a-Judge | Taught with its failure modes — position, verbosity and self-preference bias — and validated against human grades | Presented as the answer, unvalidated |
| RAG evaluation | Retrieval and generation evaluated separately, plus stale, contradictory and cross-tenant cases | One end-to-end accuracy number |
| Safety testing | A full module — direct and indirect injection, PII, secrets, system-prompt disclosure, regression suite | A slide on prompt injection |
| Cost and latency | First-class test metrics, including fallback-model testing | Not measured |
| Deliverable | A working CI evaluation harness with baselines and thresholds | Notebook exercises |
| Entry honesty | States that automation and Python experience are genuinely required | Open to anyone, which produces a high drop-out rate |
The question worth asking: does the course make you validate the judge? An unvalidated judge is an untested test tool making release decisions, and that is the failure mode this field keeps repeating.
LLM & RAG Testing or Agentic AI Testing — which comes first?
This one, almost always. LLM and RAG testing is about evaluating what a model says; agentic testing is about evaluating what an agent does — the tools it calls, the path it takes, the state it changes. Everything in the agentic course assumes you can already define a quality dimension, build a golden dataset and validate a judge, because agent evaluation uses all three and adds trajectory and tool-call analysis on top.
Take Agentic AI Testing first only if your product is already an agent and you have LLM evaluation experience from work rather than from a course. Otherwise the sequence is this course, then agentic. Learners who invert it spend the first fortnight of the agentic course learning what this one teaches, at a worse pace.
A different question is whether you want the developer side instead. Our Generative AI and Agentic AI courses under AI & GenAI teach building these systems. This course teaches evaluating them. Both are legitimate careers and the skills overlap less than people expect — building rewards making it work, evaluating rewards finding where it does not.
What are the prerequisites, and how do you start?
You need software-testing fundamentals, basic Python — writing functions, running pytest — and API-testing familiarity with REST, JSON and authentication. You do not need machine-learning knowledge, mathematics, or any experience building AI systems; the architecture module covers what a tester needs and stops there. A candidate with strong automation experience but no Python can enter after a short assessment, though we would usually suggest Selenium with Python first. If your team has already shipped an AI feature, bring a real example — the course works considerably better against your own system than against ours.
- Confirm you can write a Python function and run a pytest file
- Call a model API once from a script, so the first week is not spent on setup
- Write down how your team currently verifies its AI feature — that gap is the course
- Download the full syllabus above and check module 6 and module 9 against what you need
- Book a free counselling call if you are unsure between this and AI-Assisted Testing
Frequently Asked Questions
What is LLM testing?
LLM testing evaluates software whose behaviour depends on a language model.
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Because the same prompt can produce different valid answers, exact-match assertions do not work. Instead you define quality dimensions — correctness, relevance, groundedness, safety, format — build golden datasets with rubrics, and grade responses using deterministic checks, semantic similarity, human review and model judges.
What is RAG testing?
RAG testing evaluates both halves of a retrieval-augmented system: whether retrieval found the right context, measured by relevance, context precision and recall; and whether the answer used it, measured by faithfulness, groundedness and citation correctness.
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It also covers missing, contradictory and stale documents, and tenant-isolation tests for cross-user data exposure.
How is this different from the AI-Assisted Software Testing course?
AI-Assisted Testing uses AI as a helper to test ordinary software — one month, no Python required.
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This course tests software that is itself built on AI: hallucination, grounding, RAG quality, safety and model regression. Different jobs. If your product has a chatbot or document assistant, this is the course you need.
Do I need Python for this course?
Yes — enough to write functions and run pytest. The automation, RAG evaluation and capstone modules are all Python.
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You do not need machine-learning knowledge or mathematics. If you have strong automation experience in another language, take a short assessment with us, or take Selenium with Python first.
What is LLM-as-a-Judge, and can you trust it?
It uses one model to grade another's output against a rubric.
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It scales where human review cannot, and it has real biases — position bias, verbosity bias and self-preference. The course teaches validating a judge against human grades on a sample before trusting it. An unvalidated judge is an untested tool making release decisions.
How do you test something that gives a different answer every time?
You stop asserting equality and start evaluating properties.
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Some checks stay deterministic — schema validity, required or forbidden content, format and length. The rest are graded on defined dimensions against a golden dataset, run multiple times, and compared to a stored baseline. Regression means the score moved, not that a string changed.
How long is the LLM & RAG Testing course and what does it cost?
Two months — eight weeks of taught content plus the capstone harness, in evening or weekend batches.
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Fees sit in the ₹25,000 to ₹35,000 band with EMI available. Model API usage is not included and typically costs under ₹500 across the whole course. Call 9822052088 for current batch dates.
Which evaluation frameworks does the course teach?
You write custom Python evaluators first, then study DeepEval, RAGAS, Promptfoo and LangSmith as examples of the same patterns packaged differently.
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That order is deliberate: this tooling turns over quickly, and a course built on one vendor's API stops being useful within a year. The concepts transfer; the APIs will change.
Does the course cover AI safety and prompt injection?
Yes, as a full module — direct injection, indirect injection through retrieved documents, jailbreaks, unsafe output, PII and secret leakage, system-prompt disclosure, and role and data-isolation boundaries.
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You build a safety regression suite, because a safety fix that is not regression-tested does not stay fixed. This is the first question customers' security reviews ask.
Should I take this before or after Agentic AI Testing?
Before, in almost every case. Agent evaluation assumes you can define a quality dimension, build a golden dataset and validate a judge, then adds trajectory and tool-call analysis.
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Learners who take the agentic course first spend its first fortnight learning what this course teaches, at a worse pace.
Is this course for testers or for AI developers?
Both attend, and the mix is useful. Testers bring test design and adversarial instinct and need the AI architecture.
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Developers and AI engineers know the architecture and need evaluation discipline — golden datasets, rubrics, baselines and regression. The course assumes testing fundamentals, so developers should expect the test-design modules to be new.
Where is the LLM & RAG Testing course conducted in Pune?
At Archer Infotech in Kothrud, Pune, with evening and weekend classroom batches and live online batches for working professionals.
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Online learners get the same code review on their capstone harness repository. Batch size is capped at 15, and new batches run roughly every six weeks.
Learn LLM & RAG Testing Online or at Our Pune Centre
Good news — LLM & RAG Testing is available in live online mode as well as classroom training at our Kothrud, Pune centre. Learn from anywhere with the same trainers, curriculum and placement assistance. Register now or send us your enquiry.