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Scope of the Course

This is a practical Generative AI Application Development certification program. Students will learn how to design, build, and deploy AI applications that solve real business problems. By the end of the course, students will be able to:

  • Understand how LLMs work
  • Use OpenAI and Gemini APIs
  • Write effective prompts for business use cases
  • Build RAG-based knowledge assistants
  • Store and search documents using vector databases
  • Create AI chatbots for websites and internal teams
  • Build AI agents that can use tools and perform tasks
  • Extract information from PDFs, resumes, invoices, and business documents
  • Connect AI applications with APIs and databases
  • Understand AI safety, privacy, and hallucination control
  • Optimize token usage and API cost
  • Deploy AI applications for real users

This course is suitable for learners who want to start or grow their career in GenAI development, AI automation, AI product development, chatbot development, AI engineering, and LLM-powered application development.

Jobs Icon

Jobs You Can Apply For

  • Generative AI Developer
  • AI Application Developer
  • LLM Application Developer
  • AI Engineer
  • Prompt Engineer
  • RAG Developer
  • AI Automation Developer
  • Chatbot Developer
  • AI Product Associate
  • Junior AI/ML Engineer
  • AI Integration Developer
  • AI Solutions Developer
Curriculum Icon

Curriculum in brief

Learn through structured modules covering LLMs, APIs, prompt engineering, RAG, vector databases, AI agents, MCP basics, document AI, chatbot development, AI safety, deployment, and real-world GenAI projects.

Learning Modules
12
Learning Modules
Tools & Technologies
18+
Tools & Technologies
Job-Ready Training
6 Months
Job-Ready Training
Doubt Support
1-on-1
Doubt Support
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Tools & Technologies Covered
Python Python
JavaScript Basics JavaScript Basics
OpenAI API OpenAI API
Gemini API Gemini API
Claude Usage Basics Claude Usage Basics
LangChain LangChain
LlamaIndex LlamaIndex
Vector Databases Vector Databases
Pinecone Pinecone
Chroma Chroma
MCP Basics MCP Basics
FastAPI FastAPI
Flask Flask
Streamlit Streamlit
Node.js Basics Node.js Basics
Postman Postman
GitHub GitHub
Modules Icon

What You’ll Learn

  • What is Generative AI
  • How LLMs work
  • Tokens and context windows
  • Prompt, completion, and response flow
  • Text generation basics
  • Limitations of LLMs
  • Hallucination and accuracy issues
  • Real-world GenAI use cases
  • AI application architecture

  • OpenAI API basics
  • Gemini API basics
  • API keys and authentication
  • Request and response structure
  • Chat completion flow
  • System, user, and assistant messages
  • Temperature and model parameters
  • Streaming responses
  • Error handling
  • API usage best practices

  • Prompt structure
  • Role-based prompting
  • Instruction writing
  • Few-shot prompting
  • Chain-of-thought style task breakdown
  • Output formatting
  • JSON response prompting
  • Prompt testing and improvement
  • Business prompt templates
  • Prompt libraries for teams

  • What is RAG
  • Why RAG is used
  • Document upload flow
  • Text extraction
  • Chunking
  • Embeddings
  • Vector search
  • Retrieval process
  • Answer generation with sources
  • Reducing hallucination
  • Building knowledge-base chatbots

  • What is a vector database
  • Embeddings and similarity search
  • Storing document chunks
  • Searching relevant content
  • Metadata filtering
  • Pinecone basics
  • Chroma basics
  • FAISS basics
  • Vector database use cases
  • Managing document updates

  • Why frameworks are used
  • LangChain fundamentals
  • Chains
  • Tools
  • Memory basics
  • Retrievers
  • LlamaIndex basics
  • Document loaders
  • Query engines
  • Building modular AI workflows
  • When to use frameworks and when not to use them

  • What are AI agents
  • Agent planning basics
  • Tool calling
  • Multi-step task execution
  • Agent memory basics
  • Human-in-the-loop flow
  • Agent safety checks
  • Workflow agents
  • Business automation agents
  • Limitations of agents

  • What is MCP
  • Why MCP is useful
  • Tool-connected assistants
  • Connecting AI with external systems
  • MCP server basics
  • MCP client basics
  • Use cases for developers
  • Future of AI tool integration
  • Safe tool execution concepts

  • PDF data extraction
  • Invoice extraction
  • Resume extraction
  • Form extraction
  • Text cleaning
  • Structured data output
  • JSON extraction
  • Validation rules
  • Document summarization
  • Business document automation

  • Website chatbot flow
  • Internal company chatbot
  • FAQ chatbot
  • Knowledge-base chatbot
  • Support chatbot
  • WhatsApp chatbot basics
  • Conversation design
  • Fallback responses
  • Lead capture flow
  • Admin handoff
  • Chatbot testing

  • Prompt injection basics
  • Data leakage risks
  • PII handling
  • Hallucination reduction
  • Model selection
  • Token cost calculation
  • Prompt optimization
  • Caching responses
  • Logging and monitoring
  • Safe deployment checklist

  • Building simple AI APIs
  • Frontend and backend integration
  • Deploying AI applications
  • Environment variables
  • API key security
  • Monitoring usage
  • Handling errors
  • User feedback collection
  • Improving AI responses
  • Demo and documentation preparation

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