Generative AI Course in Visakhapatnam
Learn to build real AI-powered applications using Large Language Models, Prompt Engineering, LangChain, RAG pipelines, and autonomous AI Agents — no prior AI experience needed.
Why Learn Generative AI?
GenAI is the most in-demand skill across every industry right now
Companies in every sector — finance, healthcare, education, retail, and software — are actively hiring people who can build and deploy AI-powered tools. This course gives you exactly those skills.
Go from prompts to production — learn the full GenAI stack
Students learn how LLMs work, how to engineer precise prompts, how to connect AI to external data using RAG, and how to build autonomous AI agents that complete multi-step tasks automatically.
Build working AI apps — not just theory
Every module ends with a deployable project: a customer support bot, a document Q&A system, an AI research assistant, or a multi-tool AI agent — real outputs that go straight into your portfolio.
GenAI Skill Snapshot
How large language models work, think, and generate
Precision prompting, few-shot, chain-of-thought, and guardrails
Connect AI to your own documents and knowledge bases
Autonomous agents that plan, reason, and take actions
Generative AI Curriculum
The curriculum is organized into clear learning stages so students progress from understanding how AI models work to building and deploying complete, production-ready GenAI applications.
Understand what Generative AI is, how modern language models are trained, and why they're transforming every industry.
- What are Large Language Models (LLMs) — GPT, Claude, Gemini, and Llama explained simply
- Tokenization, context windows, temperature, and how models generate text
- Real-world GenAI use cases: chatbots, code assistants, document tools, and creative apps
Master the skill that unlocks 10× better results from any AI model — precision prompting for business, coding, and creative tasks.
- Zero-shot, few-shot, and role-based prompting techniques
- Chain-of-thought (CoT) prompting, system instructions, and output formatting
- Prompt evaluation, iteration strategies, and avoiding hallucinations
Use LangChain — the most popular GenAI framework — to build structured, multi-step AI applications quickly and cleanly.
- LangChain chains, prompt templates, memory, and output parsers
- Connecting LLMs to APIs, tools, and external data sources
- Build a fully functional AI assistant from scratch using GPT and LangChain
Learn RAG — the technique that lets AI answer questions from your own documents, databases, and private knowledge sources.
- Text chunking strategies, embeddings, and vector databases (FAISS, Chroma)
- Build a document Q&A system that retrieves accurate answers from PDFs and files
- Tuning retrieval accuracy, handling large corpora, and reducing hallucinations
Build autonomous AI agents that don't just respond — they plan, call tools, search the web, and complete complex tasks on their own.
- ReAct agent framework: reasoning + acting loops and tool calling
- Give AI agents access to web search, calculators, code runners, and APIs
- Multi-agent workflows: orchestration, delegation, and memory between agents
Bring everything together in a complete, deployable GenAI application — and prepare your portfolio and profile for the job market.
- End-to-end capstone: design, build, test, and deploy a full GenAI application
- Deploying with Streamlit, Gradio, or FastAPI — shareable and live on the web
- Portfolio review, GitHub presentation, and GenAI interview preparation
Tools You'll Master
After This Program, You Will:
Understand how LLMs work and how to choose the right model for any use case
Write expert-level prompts using system instructions, few-shot examples, and chain-of-thought strategies
Build AI-powered chatbots, assistants, and pipelines using LangChain and the OpenAI API
Design and deploy RAG systems that let AI answer questions from any document or knowledge base
Build autonomous AI agents that use tools, search the web, and complete multi-step tasks independently
Ship a live GenAI application using Streamlit or FastAPI and present a portfolio ready for the job market
Where This Takes You
Generative AI Engineer
Build and ship LLM-powered products for startups and enterprise teams
Prompt Engineer
Design precision prompting systems and AI interaction workflows
AI Solutions Architect
Plan and deploy end-to-end GenAI solutions aligned to business goals
LLM Application Developer
Build robust AI products with evaluation pipelines and API integrations
Your Journey — Week by Week
Weeks 1–2
AI basics, LLM fundamentals, and how GenAI works
Weeks 3–4
Prompt engineering mastery and output control
Weeks 5–6
LangChain apps, chains, and memory systems
Weeks 7–10
RAG pipelines, vector DBs, and AI agents
Weeks 11–12
Capstone project, deployment, and portfolio launch
Ready to Build Real Generative AI Products?
Next batch starts June 2026. Limited seats available. Book your free demo class and explore how we teach GenAI through hands-on, project-driven learning that gets you job-ready fast.