10,000+Skillsbiz learners trained
Generative AI · Enrolments open
Move beyond AI demos—build applications people can use.
Understand language models, design reliable prompts, work with leading model APIs and build LLM applications using LangChain, retrieval-augmented generation, vector databases, multimodal systems and agentic workflows.
✓Live online✓Weekly 1:1 mentorship✓Maximum 20 learners
Program fee
₹90,000+ GST · EMI options availableApplied learning
8 projectsincluding a final capstoneProgram duration
4 months
The complete journey includes teaching, practice, assessments, projects and career preparation.
Structured curriculum
16 weeks · 5 modules
From LLM foundations and prompting through RAG, multimodal systems and agents.
Applied learning
8 projects
Realistic Indian business contexts using public or synthetic datasets.
Program fee
₹90,000 + GST
Pay through up to 12 zero-interest EMIs, subject to final provider terms.
4.8/5Skillsbiz learner rating
8Projects in every program
20Maximum learners per cohort
Small live cohorts
Every cohort is limited to 20 learners to support interaction, questions and individual attention.
Flexible batch options
Choose from monthly weekday or weekend cohorts delivered live online.
Eight portfolio projects
Apply the curriculum through realistic briefs, documented case studies and a final capstone.
Weekly 1:1 mentorship
Review concepts, project decisions and your learning progress in recurring individual sessions.
90-day career support
Prepare your resume, LinkedIn profile, portfolio and interviews with structured support after completion.
Long-term learning access
Keep lifetime access to learning materials and revisit class recordings for 12 months after completion.
From enrolment to career preparation
A structured path from foundations to portfolio-ready work.
The pace varies by program, but every pathway follows the same sequence: establish direction, build core skills, complete reviewed work and prepare to present it professionally.
Program start
01Establish your direction
Assess & Plan
Review your starting point, target roles and program expectations before beginning the core curriculum.
BaselineRole directionLearning plan
Core curriculum
02Build the fundamentals
Learn & Practise
Develop technical and analytical fluency through live classes, guided exercises and regular mentor reviews.
Live classesGuided practiceMentor reviews
Project phase
03Apply what you learn
Build & Review
Complete eight realistic projects, strengthen them through feedback and document the clearest work for your portfolio.
Case studiesWritten feedbackPortfolio
Final phase + 90 days
04Prepare for opportunities
Present & Apply
Refine your resume and LinkedIn profile, complete three mock interview rounds and receive 90 days of placement assistance.
ATS resumeMock interviewsPlacement support
Who this program is for
Choose Generative AI when you want to build with language models.
This program suits learners who want to create AI-powered applications, internal tools, product features, knowledge systems or workflow automations. You should be willing to work with Python, APIs and application logic.
Generative AI or Data Science & AI?
Choose Data Science & AI for broader depth across mathematics, classical machine learning, deep learning, computer vision and NLP. Choose this program for a focused route into modern LLM application patterns, RAG, multimodal systems and agents.
Students and recent graduates
Build practical experience with prompts, model APIs, retrieval and application development through reviewed projects.
Technical career startDevelopers and engineers
Learn how model calls, retrieval, tools and application state fit together in a reliable user-facing system.
Software to AI appsWorking data and product professionals
Extend Python, analytical or product experience into embeddings, LLM workflows, evaluation and AI product development.
Professional upskillCareer switchers with coding foundations
Follow a focused application-building path with repeated prototyping, failure analysis and portfolio development.
Career transitionApplication-building outcomes
Progress from prompt experiments to well-designed AI applications.
Learn to combine models with context, tools, constraints, evaluation and clear recovery paths when an output is uncertain or unsupported.
01 / FRAME
Choose the right model pattern for the job.
Distinguish when a direct model call is enough and when retrieval, tools, workflows or human review are necessary.
02 / CONTROL
Design inputs and outputs deliberately.
Use context, examples, schemas and constraints to make application behaviour more predictable and easier to test.
03 / GROUND
Connect model output to trusted knowledge.
Understand embeddings, retrieval and vector search so answers can be built around relevant source material.
04 / EVALUATE
Inspect quality beyond a convincing demo.
Define test cases, failure modes, guardrails and review signals so the system can improve intentionally.
Interactive architecture lab
The model is one component. Design the rest of the system.
Compare four common application patterns and see how retrieval, tools, evidence, human review and evaluation requirements change with the use case.
RAG patternArchitecture map
Ground answers in a controlled knowledge source.
A knowledge assistant combines user intent with retrieval from an approved document set before asking the LLM to compose the answer.
Design question: How will the user inspect the evidence behind an answer?
Understand what generative systems do, how language models have evolved and why transformer-based models behave differently from conventional software.
Key topics
Introduction to generative AIEvolution of language modelsTransformersLLM foundations
Applied work: explain the path from a user instruction to model output, including tokens, context, model limitations and where human verification belongs.
Treat prompts as interface design. Learn to specify role, context, constraints, examples, structure and verification so model behaviour becomes more deliberate.
Key topics
Prompt designAdvanced promptingChain-of-thought conceptsFew-shot & zero-shot approaches
Applied work: design and test a reusable prompt workflow with explicit inputs, output schema, evaluation criteria and failure-case examples.
Move from chat-window experimentation to applications that call models through APIs, manage inputs and outputs and expose useful product behaviour.
Key topics
OpenAI APIClaude APIGemini APILangChainChatbotsAI assistants
Applied work: build an LLM-powered application with clear system instructions, structured outputs, application logic and observable failure handling.
Ground model answers in external knowledge. Learn the architecture of chunking, embeddings, retrieval, vector search and context assembly behind useful knowledge assistants.
Key topics
Retrieval-augmented generationEmbeddingsPineconeChromaDBKnowledge bases
Applied work: index a document collection, retrieve relevant evidence and generate answers that expose the supporting context for review.
Combine earlier building blocks into richer systems spanning media generation, multimodal inputs, model adaptation and multi-step AI workflows.
Key topics
Image generationMultimodal applicationsFine-tuningAgentic workflows
Applied work: build a multi-step GenAI application with an explicit architecture, evaluation plan, safety controls and technical walkthrough.
From prompt to product
A useful GenAI system needs more than a model endpoint.
The technical details vary by application, but strong systems usually need deliberate choices around context, retrieval, tools, validation and operating controls.
Prompt & context
Specify intent, available context, constraints, examples and the output shape the application can safely consume.
Retrieval & memory
Bring relevant external information into context through embeddings, vector search and deliberate chunking/retrieval choices.
Tools & workflow
Let the application call functions, APIs or specialist steps when generation alone cannot complete the task reliably.
Evaluation & guardrails
Create test sets, review outputs, detect unsupported answers and decide where human approval must remain in the loop.
Eight portfolio projects
Build applications that reveal your engineering decisions.
Develop practical applications using public or synthetic data in realistic Indian business contexts. Each project focuses on architecture, evaluation and responsible application behaviour, culminating in an end-to-end capstone.
P/01Knowledge
Evidence-Linked Policy Assistant
Build a document Q&A assistant that retrieves relevant source passages before producing an answer and exposes those passages for review.
Design focus: chunking, retrieval quality, citations and unsupported-answer handling.
PythonRAGVector DB
P/02Support
Tool-Using Service Assistant
Create an assistant that can answer policy questions but also call approved functions such as ticket creation, order lookup or escalation.
Design focus: function schemas, permissions, confirmation steps and fallback behaviour.
LLM APIToolsFastAPI
P/03Research
Source-Aware Research Workspace
Orchestrate search, extraction, note synthesis and evidence tracking into a structured research workflow with human checkpoints.
Design focus: decomposition, provenance, evidence quality and human approval.
AgentsRetrievalEvaluation
P/04Analytics
Natural-Language Analytics Assistant
Translate a user question into a controlled analytics workflow that produces structured query intent, checks assumptions and explains the result.
Design focus: schema awareness, safe query generation, ambiguity and review.
PythonSQLLLM
P/05Agents
Multi-Step Operations Agent
Design a bounded agent that plans a task across a small set of tools, records intermediate steps and stops for approval when risk increases.
Design focus: tool boundaries, state, termination rules and auditability.
Agent loopToolsGuardrails
P/06Multimodal
Multimodal Content Assistant
Build an application that combines text and image inputs to interpret, organise or transform user-provided content.
Design focus: input quality, cross-modal failure analysis, fallback behaviour and user review.
OpenAI APIDALL-EStreamlit
P/07Evaluation
Model Adaptation & Evaluation Lab
Compare prompting, retrieval and fine-tuning approaches for a defined task using a repeatable evaluation set.
Design focus: adaptation strategy, quality metrics, cost, latency and documented trade-offs.
Hugging FacePythonEvaluation
P/08Capstone
Production-Minded GenAI Application
Combine model selection, prompting, retrieval or tools, a usable interface and an evaluation suite into one end-to-end capstone.
Design focus: architecture trade-offs, cost and latency awareness, testability, safety and documentation.
LLM stackEvaluationDeployment
GenAI toolkit
Build across models, orchestration, retrieval, interfaces and delivery.
Core work uses Python, OpenAI API, LangChain, Hugging Face, Pinecone, Streamlit, FastAPI, DALL-E and Stable Diffusion. Supporting tools are introduced where they serve a specific application or workflow.
Python
OpenAI
LangChain
Hugging Face
GitHub
AWS
Anthropic Claude
Google GeminiPinecone
Qdrant
ChromaDBFAISS
LlamaIndex
FastAPI
Streamlit
Docker
CrewAIDALL-E
Stable Diffusion
GradioAWS Bedrock
Evaluation
Learning & career support
Structured support throughout and beyond the program.
Receive individual guidance while you learn, written feedback on application architecture and practical career preparation as you begin presenting your projects to employers.
Live learning support
Stay connected to the curriculum through live teaching, recordings and long-term access to learning support.
Weekday and weekend live batchesRecordings for 12 months after completionLifetime learning-material accessFive years of structured doubt support
Mentorship & project review
Work directly with a mentor to strengthen your prompts, retrieval logic, application architecture and portfolio presentation.
Weekly one-to-one mentorshipWritten feedback on portfolio workReviewed capstone and GenAI applicationsRepeat-batch access for up to five years
Career preparation
Prepare to explain your application architecture, evaluation approach and project work clearly during applications and interviews.
Resume, LinkedIn and portfolio guidanceThree mock interview roundsOne-year internship pathway and LOR90 days of active placement assistance
Career directions
Explore roles focused on applied LLM development.
The curriculum develops capabilities relevant to the following roles. Completing the program does not guarantee a particular job title or employment outcome.
What your portfolio should demonstrate
Show that you understand context, retrieval, tools, evaluation and failure modes—and can turn model capability into controlled application behaviour.
Generative AI Engineer
Build model-powered applications, integrate APIs, orchestrate workflows and improve reliability through testing and evaluation.
LLM Developer
Design prompts, retrieval pipelines, knowledge ingestion and grounded answer-generation workflows.
AI Product Engineer
Combine product requirements with software and LLM-system design to prototype and build AI-enabled features.
AI Automation Developer
Use tools, APIs and bounded agents to automate multi-step workflows with explicit controls and review points.
GASchool of Data Science · Skillsbiz Education
Certificate of Completion
Generative AI
Issued and digitally verifiable by Skillsbiz Education after the program completion requirements have been met.
Skillsbiz Education certificate
Receive a digitally verifiable program certificate.
After meeting the completion requirements, you will receive a Certificate of Completion in Generative AI, issued and digitally verifiable by Skillsbiz Education.
✓Records the specific program completed.✓Issued by Skillsbiz Education.✓Verifiable through the official Skillsbiz certificate portal.
Certificate verification: Use the official Skillsbiz Education portal to verify certificates issued by the company. Open verification portal ↗
Program FAQs
Questions before choosing Generative AI.
Review the starting requirements, duration, fees, learning format and career support before you apply.
Compare all four programsThis is positioned as an application-building pathway, so comfort with Python and basic software concepts is useful. Learners who want a lower-code entry point into data work may find Data Analytics + Gen AI more direct.
The program runs across four months, with 16 weeks of structured curriculum delivered through five sequential modules. The wider journey includes scheduled teaching, guided practice, assessments, eight projects, capstone work and career preparation.
Data Science & AI is the broader 36-week modelling pathway across mathematics, classical ML, advanced ML, deep learning, computer vision, NLP and MLOps. Generative AI is a shorter focused track around LLM foundations, prompting, LLM applications, retrieval and advanced GenAI application patterns.
No. In a typical RAG architecture, relevant information is retrieved from an external knowledge source and supplied as context at generation time. The model does not need to be retrained simply to use that retrieved context.
No. You will work with leading model APIs and representative frameworks while learning transferable concepts in prompt design, retrieval, evaluation, tool use and application architecture.
The program fee is ₹90,000 + GST, with up to 12 zero-interest EMIs available. New live online cohorts begin monthly, with weekday and weekend batch options. Admissions will confirm the current timetable and payment-provider terms before enrolment.
Career support includes resume, LinkedIn and portfolio guidance, three mock interview rounds, job-portal access, referrals and placement drives, followed by 90 days of active placement assistance. These services support your job search but do not guarantee employment.
Yes. After meeting the completion requirements, you will receive a Certificate of Completion in Generative AI, issued and digitally verifiable by Skillsbiz Education.
Ready to build practical generative AI applications?
Apply now, book a free counselling session or request the detailed brochure to review the curriculum, projects, fee and next available batches.
Speak with admissions
Tell us about your technical background and the AI applications you want to build.
An admissions advisor will help you assess program fit, prerequisites, batch options and payment terms. Expect a response within 12 hours during working hours: 10:00 AM–6:00 PM, Monday–Saturday.
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