About Course
About Generative AI Training
Tokens, context windows, evaluation and guardrails — how a language model actually behaves before you put it in front of a customer.
Most generative AI training is a tour of tools. This one starts from what a model actually is — a token predictor with no database behind it — because that is the fact that explains everything else: why a confident wrong answer sounds like a right one, why temperature changes a summary rather than fixing it, and why an evaluation set has to be built from real traffic instead of from the examples that happened to work in a demo. Practitioners who never learn this spend months treating hallucination as a bug to be patched rather than a property to be measured and bounded.
Course Overview
12-week Generative AI training (72 hours) covering transformers, tokens and context windows, prompt design, evaluation methods, hallucination, safety guardrails and model cost. Classroom, Live Online and Hybrid.
Why Learn Generative AI Training
Nearly every function now needs someone who can use a model without embarrassing the company: the person who can tell a useful prompt from a plausible one, catch the invented citation before the customer does, and say what a feature costs per thousand requests before it ships. That judgement is scarce because it is taught badly, and it is paid for across consulting, product, marketing-operations, legal and compliance teams that cannot hire a machine-learning engineer for every problem.
It also supplies the vocabulary every technical track after this assumes. Tokens, evaluation, grounding, policy and cost are the same four concerns the retrieval, agent and MLOps courses start from, so learners who begin here stop guessing at why an application behaves the way it does. The governance side matters just as much: teams asked to sign off on AI use need someone who can describe what is sent, what is stored and what could leak.
Who Should Join This Course
- Product Managers — Deciding what a language model can and cannot be trusted to do
- Business Analysts — Turning a vague AI idea into a scoped, testable use case
- Marketing and Content Teams — Using generated text without shipping invented facts
- Software Developers — Getting the foundations before moving into application build
- Fresh Graduates — Entering AI work without a machine learning background
- Risk and Compliance Staff — Assessing an AI feature before it reaches customers
Prerequisites
No AI or coding experience is required. You should be comfortable reading a business process description and working with structured documents. Anyone who has written a report, a policy or a specification will find the prompt material familiar, because good prompting is specification writing with a machine on the other end.
Learning Objectives
- Explain how a transformer produces a token and why the answer is not a lookup
- Size a prompt with role, context, constraints and an explicit output contract
- Choose an evaluation method that measures the behaviour you actually care about
- Detect and reduce hallucination with grounding, citations and refusal behaviour
- Set a confidence and escalation path for answers a model should not guess
- Budget a feature in tokens and state its cost per thousand requests
- Write a safety policy covering prompt injection, data leakage and harmful output
- Take a use case from idea to a tested, costed, gated proposal
What the Syllabus Covers
Each module is assessed against published Generative AI Fundamentals outcomes rather than on memorisation, covering Transformers, Prompt Design, Evaluation, Hallucination Detection, Guardrails, Model Costing together with a prompt-specification method, an evaluation set built from real traffic, a red-team checklist for prompt injection and data leakage, and a written recommendation you can defend in a review.
Career Opportunities After This Programme
- Generative AI Analyst
- AI Prompt Engineer
- AI Product Analyst
- Solution Consultant (GenAI)
- AI Content Strategist
- Responsible AI Analyst
Hands-on Practice and Capstone
Capstone: Take a real business process to a decision: define the prompt contract and output schema, build an evaluation set from genuine examples and score the first version, add grounding with citations and re-measure the hallucination rate, write the safety and prompt-injection policy with a human escalation gate, then present the per-request cost and a defensible go-or-no-go recommendation.
Production scenario: A team shipped a customer-facing assistant that confidently cites a refund policy the company does not have. The course treats that as the normal failure mode rather than bad luck: the fix is an eval set built from real tickets, grounding that forces a citation for every policy claim, a refusal path for anything ungrounded, and a measured hallucination rate tracked across releases instead of a demo that looked fine on ten examples.
Course Duration, Mode and Certification
The course runs for 12 weeks (72 hours), three sessions a week of two hours each. Modes: Classroom / Live Online / Hybrid. Certificate issued: SkilBrill Course Completion Certificate. Assessment is against published Generative AI Fundamentals outcomes, and you finish with evidence you can show rather than a pass mark.
Why Train at SkilBrill
SkilBrill Training Institute in Thoraipakkam focuses on making you employable. Trainers are working AI, data and platform engineers, every module produces portfolio evidence rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹45,000 – ₹75,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to AI, data and platform engineering roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering evaluation, pipeline design and system-review scenarios
- Capstone walkthrough and portfolio review preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹6 – 16 LPA
Frequently Asked Questions
What does the Generative AI training at SkilBrill cover?
Twelve weeks and 72 hours over eight modules: how transformers and tokens work, prompt design as engineering, evaluation, grounding and hallucination, safety and prompt injection, cost and latency, applied practice, and a capstone that ends in a costed go-or-no-go proposal.
Do I need to code for this course?
No. This is the practitioner and governance side of generative AI rather than the application build. You will work with prompts, evaluation spreadsheets and scorecards, and read enough code to review what a developer shows you. If you want to build LLM applications in Python with retrieval, that is the separate LLM Application Development course.
How is this different from the AWS Generative AI Development course?
That course builds on Amazon Bedrock and AWS-specific services. This one is platform-neutral and spends its time on prompting, evaluation, hallucination, safety and cost, which behave the same whichever provider you use. The two cross-link rather than compete, and you can take either first.
Is certification included?
The syllabus follows generative AI fundamentals objectives so it works as preparation for foundation-level credentials. Vendor exams are booked through their own programmes, and the SkilBrill completion certificate is issued by us on the capstone.
What is prompt injection and why is it a full module?
It is an attack where instructions hidden in a web page, email or uploaded document override your system prompt. It is the most common real security failure in deployed AI, so the module covers direct, indirect and document-borne variants plus the filtering and escalation policy that limits the damage.
Will I be able to evaluate an AI feature critically?
That is the point of the course. You will build an evaluation set from real traffic, score format compliance and factuality separately, and recognise the moment a benchmark rises while the product actually gets worse for the user.
What kind of roles does this lead to?
Generative AI analyst, prompt engineer, AI product analyst, solution consultant and responsible AI analyst roles, plus product and risk positions that need to judge AI features rather than build them.