Generative AI Course Online with RAG, Fine-Tuning & Projects
Learn to build AI applications that answer questions from documents, adapt language models to specific tasks, and work with text and images. This 24-week live online Generative AI course combines six guided projects, project reviews and recordings, with a course-completion certificate and career support.
Designed for software developers, ML practitioners and technical professionals with basic Python knowledge.
Course at a Glance
24 Weeks
Live online classes
Live online
Instructor-led
6 Projects
Guided builds
Project Reviews
Mentor feedback
Recordings
Included
Fee: ₹64,999
Confirm applicable taxes and payment terms with admissions.
Generative AI Course Overview: Format, Duration and Outcomes
The School of Core AI Generative AI Course is a 24-week live online specialization covering RAG engineering, LoRA/QLoRA fine-tuning, multimodal AI, LLM evaluation and serving foundations. Learners complete 6 guided systems, receive a course-completion certificate, and can access placement and career support. The fee is ₹64,999.
UNDERSTAND
Transformers & GenAI Models
GROUND
RAG & Retrieval
ADAPT
LoRA / QLoRA
EVALUATE
Quality & Failure Analysis
INTEGRATE
Serving & Applications
- Mode
- Live online cohort with recordings and project review
- Duration
- 24 weeks / 6 months
- Fee
- ₹64,999. Confirm applicable taxes and payment terms with admissions.
- Audience
- Developers, ML practitioners and technical working professionals
- Projects
- 6 guided systems
- Certificate
- School of Core AI course completion certificate
Who Should Join This Generative AI Course?
This course is best suited to software developers, ML practitioners and technical working professionals who know basic Python and want deeper capability in RAG, model adaptation, multimodal AI and evaluation. It is not positioned as a no-code AI-tools course for non-technical beginners. For a software background, see how existing engineering skills map to GenAI work.
Generative AI Course for Software Developers
Want to move beyond API integration into retrieval quality, model adaptation and evaluation.
Background: Basic Python, some backend or app experience
Gains: RAG, fine-tuning, multimodal AI and evaluation capability
Generative AI Course for ML Engineers and Data Scientists
Want deeper RAG, fine-tuning and evaluation skills with structured project evidence.
Background: Python, basic ML concepts, comfort with notebooks
Gains: Production-grade GenAI systems with evaluation evidence
Generative AI Course for Working Professionals
Want a live online format with recordings and project review while working full-time.
Background: Technical background with Python fundamentals
Gains: Portfolio-ready GenAI systems and career support
Is This Generative AI Course Suitable for Beginners?
If you have basic Python skills and are new to Generative AI, the refresher phase helps you get up to speed. If you do not yet know Python functions, modules and virtual environments, you should build those foundations first — this course does not teach programming from scratch.
Good fit if you have
Basic Python skills
Technical mindset
Ready for project work
Interest in evaluation depth
Generative AI Course Eligibility and Prerequisites
You should understand Python functions, modules, virtual environments and basic machine-learning terminology. No prior Generative AI experience is required, but you should be comfortable writing and running Python code.
This course is not ideal if you
Want only no-code AI
Want only prompt templates
Do not want programming
Expect guaranteed placement
What You Will Learn in the Generative AI Course
By the end of the course, learners can choose an appropriate GenAI approach, build grounded and adapted systems, compare results against a baseline, identify important failure cases and communicate evaluation evidence without exposing private course solutions.
RAG and Retrieval Engineering
Build an AI assistant that finds relevant information in your documents and supports its answers with sources.
Build ingestion, chunking, retrieval, reranking and citation systems. Evaluate whether the right evidence is retrieved and grounded.
Key Areas
Chunking · embeddings · hybrid retrieval · reranking
Engineering Decision
When should retrieval be used instead of simply adding more context?
LLM Fine-Tuning with LoRA and QLoRA
Adapt a language model to a particular task, then check whether it performs better than the original model.
Adapt open-weight models with parameter-efficient methods. Compare against baselines with held-out evaluation and regression awareness.
Key Areas
LoRA · QLoRA · training/eval splits · regression checks
Engineering Decision
Does the target task improve without degrading general behaviour?
Multimodal Generative AI
Build applications that work with text and images, including documents containing both.
Build text-plus-image/document workflows. Evaluate visual grounding, field-level accuracy and abstention for unreadable inputs.
Key Areas
Vision-language · diffusion · field accuracy · abstention
Engineering Decision
Can the system honestly handle inputs it cannot read?
LLM Evaluation, Guardrails and Failure Analysis
Test whether answers are accurate, supported by evidence and appropriate for the task.
Test failure cases hidden behind fluent output. Evaluate grounding, safety and reliability before declaring a system production-ready.
Key Areas
Failure matrix · grounding checks · guardrails · abstention
Engineering Decision
What failure is hidden behind a confident, fluent response?
Model Serving and GenAI Application Integration
Make a model accessible through an API so it can be used within an application.
Cover API exposure, packaging and serving readiness. Link to LLMOps for deeper production operations.
Key Areas
FastAPI · Docker · inference frameworks · serving readiness
Engineering Decision
Is the model usable outside a local notebook demo?
Agentic AI Foundations
Explore how an AI system can choose and use tools within defined limits.
Cover bounded tool use, step limits and trace review. Deep agent engineering belongs to the dedicated Agentic AI Course.
Key Areas
Bounded tool use · step limits · trace review
Engineering Decision
When does an agent loop, and how do you stop it safely?
How You Choose Between Prompting, RAG, Fine-Tuning and Agents
Use prompting when the model already knows the required information and needs clearer instructions. Use RAG when answers depend on private, changing or citable knowledge. Use fine-tuning when repeated task behaviour must change. Use a bounded agent workflow only when the system must select and execute tools across multiple steps.
| Problem signal | Start with | What must be measured | Course location |
|---|---|---|---|
| The model knows the facts but returns inconsistent structure or style | Prompting and structured outputs | task pass rate, schema adherence and consistency | Transformer and LLM workflow |
| The answer depends on private, updated or source-cited information | RAG | retrieval recall, citation correctness and grounded answer quality | RAG Knowledge System |
| The model repeatedly fails a specialised behaviour despite good context | LoRA/QLoRA fine-tuning | held-out task accuracy, format adherence and regression | Fine-Tuned LLM Assistant |
| The task depends on images, scans, charts or mixed documents | Vision-language or multimodal workflow | field accuracy, visual grounding and abstention | Multimodal Document Intelligence System |
| The task requires controlled API or tool execution across steps | Bounded agent workflow | task success, tool-call errors, step count and loop rate | GenAI Capstone |
| The system works but must be scaled, monitored and cost-controlled | LLMOps | latency, throughput, reliability, cost and production regressions | Dedicated LLMOps Course |
GenAI demos often appear successful because the output is fluent. Engineering review starts by testing the failure hidden behind that fluency.
| System | False sign of success | Failure learners detect | Review evidence |
|---|---|---|---|
| RAG | The answer sounds correct | required passage was not retrieved or the citation does not support the answer | retrieval metrics, citation check and query-level error analysis |
| Fine-tuned LLM | Training loss decreases | the target task improves but general or safety behaviour regresses | held-out set, baseline comparison and regression suite |
| Multimodal model | The response confidently describes an image | text was unreadable, fields were missed or visual evidence was invented | field accuracy, grounding check and abstention cases |
| Agent workflow | One happy-path demo completes | a failed tool call produces repetition, duplicated work or an uncontrolled loop | trace review, maximum-step control and task success rate |
| Served model | The local API returns a response | latency, memory or concurrency makes the system unusable outside the demo | basic latency/resource report; deeper operations continue in LLMOps |
SCAI GenAI Engineering Loop: define → select → build → evaluate → explain
Notebooks, datasets, lab prompts and solutions remain learner-only.
Use this curriculum checklist to compare the depth and assessment behind a GenAI syllabus
Generative AI Projects You Will Build and Evaluate
Learners complete six guided systems that move from model behaviour and baselines to RAG, fine-tuning, multimodal AI and an integrated GenAI capstone. Public portfolio evidence shows the problem, architecture decision, evaluation method and failure analysis—not the private notebook or solution code. See how to turn a project idea into an evaluated system with the Generative AI projects guide.
Model Training and Evaluation Workflow
What you build
Train and compare a baseline model
What you submit
Training script, evaluation report and baseline comparison
How it is evaluated
Reviewed for reproducibility, evaluation methodology and whether the comparison is valid
Engineering Question
How do you prove one model/version is actually better?
Transformer Behaviour Lab
What you build
Test tokenization, context and structured-output behaviour
What you submit
Lab notebook with documented tests and findings
How it is evaluated
Reviewed for test coverage, documentation quality and failure identification
Engineering Question
Where does the model fail despite good prompts?
RAG Knowledge System
What you build
Build ingestion, chunking, retrieval, reranking and citations
What you submit
Working RAG pipeline with retrieval metrics and citation checks
How it is evaluated
Reviewed for retrieval quality, citation accuracy and grounded answer correctness
Engineering Question
Does the system retrieve and cite the right evidence?
Fine-Tuned LLM Assistant
What you build
Adapt a suitable open-weight model with LoRA or QLoRA
What you submit
Fine-tuned model adapter, evaluation set and baseline comparison
How it is evaluated
Reviewed for task improvement, regression check and evaluation rigor
Engineering Question
Did adaptation improve the target without regressing safety?
Multimodal Document Intelligence System
What you build
Process text and visual document inputs
What you submit
Multimodal pipeline with field-accuracy results and failure cases
How it is evaluated
Reviewed for field accuracy, visual grounding and honest abstention on unreadable inputs
Engineering Question
Can the system handle unreadable or ambiguous inputs honestly?
Controlled GenAI Capstone
What you build
Combine retrieval, model access, tool use, API serving and tracing
What you submit
Integrated system with architecture decision record, evaluation set and failure analysis
How it is evaluated
Reviewed for integration quality, evaluation evidence and production readiness gaps
Engineering Question
Is the integrated system ready for production handoff?
How Generative AI Projects Are Evaluated
A working demo is not considered complete merely because it produces an answer. Each major project must include a defined task and baseline, an architecture decision record, an evaluation set, a failure analysis and a portfolio-safe system walkthrough.
BUILD
Define task, baseline, architecture
TEST
Run evaluation set and failure cases
EVALUATE
Quality · Grounding · Reliability · Safety
PASS / ITERATE
Document evidence or iterate
Generative AI Course Syllabus: 24-Week Curriculum
The 24-week Generative AI course syllabus progresses from model behaviour and evaluation baselines to RAG, fine-tuning, multimodal AI, serving foundations and controlled agentic workflows. The public syllabus shows capability progression; detailed labs, datasets and solutions remain available only to enrolled learners.
Phase 1: Generative AI and Transformer Foundations
Weeks 1–6
- Python, mathematics and machine-learning refresher
- neural-network training and evaluation
- transformer, tokenization and attention behaviour
- vision and language model foundations
Output: baseline experiment report and transformer-behaviour analysis.
Phase 2: Language, Vision and Multimodal Generative AI
Weeks 7–12
- generative model families and appropriate use cases
- large language model behaviour and limitations
- vision transformers, vision-language models and diffusion foundations
- controlled prompting and structured outputs
Output: evaluated language or visual-generation workflow.
Phase 3: RAG, Embeddings and LLM Fine-Tuning
Weeks 13–18
- multimodal document workflows
- LoRA and QLoRA adaptation
- prompting vs RAG vs fine-tuning decisions
- chunking, embeddings, vector and hybrid retrieval
- reranking, citations and RAG evaluation
Output: RAG evaluation report, fine-tuning comparison and multimodal error taxonomy.
Phase 4: LLM Evaluation, Model Serving and Agentic AI Foundations
Weeks 19–24
- quantization and inference constraints
- API serving and deployment packaging
- alignment concepts, evaluation and guardrails
- bounded tool use and agentic foundations
- capstone integration and readiness review
Output: evaluated capstone, API demonstration and documented production gaps.
Generative AI Tools, Hardware and Additional Costs
Tools are introduced for a defined engineering task: adapting models, building retrieval, evaluating outputs, processing multimodal inputs, exposing APIs or testing serving readiness. The curriculum is not presented as a collection of software badges.
Tools and Frameworks Taught
| Engineering job | Current tool families |
|---|---|
| Develop and adapt models | PyTorch, Hugging Face Transformers, PEFT and TRL |
| Build retrieval systems | LangChain or LlamaIndex, FAISS and a project-appropriate vector store |
| Evaluate RAG and model outputs | RAGAS, DeepEval and task-specific human-review rubrics |
| Build multimodal workflows | selected vision-language and diffusion model families |
| Expose and demonstrate systems | FastAPI plus Gradio or Streamlit |
| Package and test serving readiness | Docker and selected inference frameworks such as vLLM or TGI |
Laptop and Hardware Requirements
You need a laptop that can run Python development environments, Jupyter notebooks and Docker containers. A GPU is not required on your local machine — model training and fine-tuning labs use cloud-based GPU access. Confirm current hardware recommendations with admissions if you are unsure.
Included Compute and API Access
Model labs use open-weight models appropriate to the available compute environment. Larger models may be analysed or accessed through APIs. Confirm with admissions what cloud credits or API access are included in the current cohort, as these may change between batches.
Additional Paid Requirements
Cloud GPU usage, paid API subscriptions (such as OpenAI or Anthropic) and any third-party tool subscriptions needed for your own project work are not included in the course fee. Budget separately for these if you do not have existing cloud credits. The course does not require every listed product as a paid subscription — open-source alternatives are used wherever practical.
Generative AI Course Fees and Payment Options
The Generative AI Course runs for 24 weeks / 6 months in a live online format with recordings and project review. The one-time course fee is ₹64,999. Confirm applicable taxes and payment terms with admissions before enrolling. Use the fee comparison guide to check inclusions and additional costs.
- Duration
- 24 weeks / 6 months
- Format
- Live online
- Projects
- 6 guided systems
- Certificate
- Included
₹64,999
Confirm applicable taxes and payment terms with admissions.
Generative AI Course with Certificate and Placement Support
Learners who meet the coursework and project requirements receive a School of Core AI Generative AI course-completion certificate. Placement and career support can include portfolio presentation, resume and LinkedIn positioning, interview preparation and opportunity sharing when available; it does not guarantee a job, interview, salary or placement timeline.
Generative AI Course Certificate and Assessment
Learners who complete the required coursework and project criteria receive a School of Core AI course completion certificate. It represents completion of the program requirements; it is not presented as a university degree, government licence or guaranteed-employment credential.
Award criteria
Projects completed and evaluated
Assessment requirements met
Capstone system demonstrated
Placement and Career Support Included
Project portfolio presentation review
Resume and LinkedIn positioning guidance
Technical interview preparation
Opportunity sharing when available
School of Core AI does not guarantee a job, interview, salary, employer or placement deadline.
AI Developer vs Generative AI vs LLMOps: Which Course Should You Choose?
Choose AI Developer when your main goal is to build AI applications and backend workflows. Choose the Generative AI specialization when you need deeper RAG quality, model adaptation, multimodal systems and evaluation. Choose LLMOps when your focus is releases, observability, scaling, governance and production reliability.
| AI Developer | Generative AI | LLMOps | |
|---|---|---|---|
| Primary goal | Build apps | GenAI model depth | Operate systems |
| RAG | Applied | Core | Production |
| Fine-tuning | Limited | Core | Release/Ops |
| Multimodal | Applied | Core | Supporting |
| Evaluation | App-level | Model/system | Production |
| Serving | API/app | Model/app | Scale/infra |
→ Build AI applications
Generative AI
→ Go deeper into GenAI models, RAG, fine-tuning and evaluation
→ Operate, serve, observe and scale LLM systems
School of Core AI Learner Reviews
These are institution-wide School of Core AI learner reviews and are not presented as reviews of one specific course.
Rated 4.7/5 on Google from 137 reviews
School of Core AI institution-wide rating. This is an institution-wide rating, not a course-specific aggregate rating. Individual verified learner reviews will be added when available.
View Google reviews →Frequently Asked Questions About the Generative AI Course
The questions people ask most before enrolling — course fit, prerequisites, syllabus depth, career outcomes, fees, certification and placement support.
Need help choosing between AI Developer, Generative AI and LLMOps?
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