Future Tech Intermediate → Advanced
GenAI Development
Build AI Apps with LLMs, RAG, Agents, MCP & Production Deployment
22 weeks 13 Modules Weekend Batch 120+ enrolled
About This Course
Hands-on program to build production-grade AI applications using LLMs, advanced RAG, agents, MCP, fine-tuning, and multimodal systems.
Generative AI & LLM fundamentalsPrompt engineering & evaluationLLM APIs & tool callingEmbeddings & vector databasesAdvanced & agentic RAGLLM evaluation & observabilityAI agents & workflow automationMCP (Model Context Protocol)Multimodal & fine-tuned modelsProduction deployment & responsible AI
What You'll Achieve
Build real-world AI applications
Create chatbots and assistants
Implement RAG systems
Deploy AI-powered applications
Build portfolio-ready AI projects
Course Curriculum
Week 1-2
Foundations of AI, ML & Generative AI
- Introduction to AI, Machine Learning & Deep Learning
- Types of ML: Supervised, Unsupervised, Reinforcement Learning
- Math essentials for AI: vectors, matrices, probability, optimization intuition
- Python for AI: NumPy, Pandas, data handling
- Introduction to Generative AI
- Understanding LLMs (GPT, Claude, Gemini, open-source models)
- Tokens, context windows & transformers (high level)
- AI tools ecosystem overview
- Real-world AI use cases across industries
- AI-assisted coding with GitHub Copilot & Cursor AI
Week 3-4
Prompt Engineering & AI Interaction Design
- Zero-shot, one-shot & few-shot prompting
- Chain-of-thought prompting
- Role prompting & instruction hierarchy
- Structured outputs & JSON generation
- Prompt templates & reusable workflows
- Prompt debugging & evaluation
- Comparing prompts systematically
Week 5-6
Working with LLM APIs
- OpenAI API and equivalent providers
- Streaming responses
- Function/tool calling basics
- Token usage & cost optimization
- Rate limits & retries
- Environment variables & API security
- Building simple AI applications
Week 7-8
Building AI Applications
- Building chatbots & assistants
- Memory handling & conversation management
- FastAPI / Flask basics
- Frontend AI interfaces: Streamlit & Gradio
- Logging, debugging & deployment basics
Week 9-10
Embeddings, Search & Vector Databases
- Embeddings & semantic search
- Cosine similarity & distance metrics
- Vector databases: FAISS, Pinecone, Weaviate
- Chunking strategies
- Hybrid search & reranking
Week 11-13
Advanced RAG Systems
- Naive → advanced → agentic RAG
- Document ingestion pipelines
- Metadata filtering & retrieval optimization
- Guardrails in RAG
- Prompt injection protection
- GraphRAG basics
- Production-grade knowledge systems
Week 14-15
LLM Evaluation, Testing & Observability
- RAG evaluation metrics
- RAGAS, TruLens & DeepEval
- A/B testing AI responses
- LangSmith tracing & monitoring
- Token usage, cost tracking & latency optimization
Week 16
AI Agents & Workflow Automation
- Agent architectures
- Tool calling & orchestration
- Planning & execution loops
- LangChain & LlamaIndex concepts
Week 17
MCP (Model Context Protocol)
- MCP architecture: Host, Client & Server
- Connecting AI agents to external tools
- Building custom MCP servers
- Tool interoperability concepts
Week 18
Generative Models Beyond Text
- Image generation & Stable Diffusion basics
- Speech-to-text & text-to-speech
- Code generation models
- Multimodal AI systems
Week 19
Fine-Tuning & Open-Source AI
- Prompting vs RAG vs Fine-tuning
- LoRA & PEFT
- Hugging Face ecosystem
- Ollama & local inference
- Model selection & quantization
Week 20
Production AI Engineering & Deployment
- Docker & deployment basics
- Caching, retries & fallbacks
- Async AI workflows
- AI security & jailbreak defense
- Responsible AI practices
Week 21-22
Capstone Project
- End-to-end production-grade AI application
- Advanced RAG / Agent workflow / MCP integration
- Mandatory deployment & evaluation pipeline
- Portfolio preparation & project presentation
Who Is This For?
Developers building AI apps
Data professionals exploring GenAI
Engineers upskilling in AI
Tech enthusiasts
Meet Your Instructor
What Our Students Say
“I had a highly positive experience completing the SQL course with AnalyticShala. The instructor is very knowledgeable, clear and responsive, ensuring an engaging and effective learning experience.”
SameerData Analyst @ Infosys
“I recently took Data Analytics using Python classes with AnalyticShala, and my experience was exceptional. The course provided a deep and practical understanding of the subject.”
NehaManager @ Genpact