Programme Content
Python for Data Science | Async pre-read content
Exploratory Data Analysis and Data Visualization | Async pre-read content
Machine Learning | Pre-read content
Deep Learning | Async pre-read content
Evolution of AI: From GenAI to Agentic AI
Building the Agentic AI Mindset & Opportunities
Introduction to LangChain & LangGraph for Agentic Design
Demonstration | Learning Objective | Required Tools
Demo 1: Your First AI Assistant
Learning Objective: Build a simple conversational assistant with a system prompt and configurable model settings.
Required Tools: LLM APIs, Streamlit
Demo 2: Prompt Playground
Learning Objective: Explore how prompts, temperature, and context influence model behavior.
Required Tools: Python, OpenAI
Demo 3: Token & Context Explorer
Learning Objective: Visualize tokenization and context window limitations.
Required Tools: Tiktoken
LLM Foundations (Transformers, Tokens, Context Windows & Scaling Laws)
Prompt Engineering (Chain-of-Thought, Few-Shot & Structured Output)
LangChain LCEL (Chains, Output Parsers & Callbacks)
System Instructions, Prompt Security & Injection Defence
Demonstration | Learning Objective | Required Tools
Demo 1: Email Writing Assistant
Learning Objective: Generate professional emails from short instructions.
Required Tools: LangChain
Demo 2: Resume Improvement Assistant
Learning Objective: Compare a resume with a job description and recommend improvements.
Required Tools: LangChain
Demo 3: Structured Data Extraction
Learning Objective: Extract structured JSON from invoices or forms.
Required Tools: LangChain
Demo 4: Prompt Injection Defense
Learning Objective: Demonstrate common attacks and practical mitigation strategies.
Required Tools: Guardrails
Agent Lifecycle: Observe – Plan – Act – Reflect Loop & State Management
Tool Use, Function Calling & Model Context Protocol (MCP)
Planning, Execution & Memory Systems
Demonstration | Learning Objective | Required Tools
Demo 1: Tool-Calling Weather Agent
Learning Objective: Answer weather queries by invoking external tools.
Required Tools: Function Calling
Demo 2: Personal Task Agent
Learning Objective: Maintain tasks with memory and planning.
Required Tools: Memory, MCP
Demo 3: Travel Planning Agent
Learning Objective: Create travel itineraries using external services.
Required Tools: LangGraph
Vector Databases, Embeddings & Similarity Search in Production
RAG in Action: Enterprise Search & AI Knowledge Systems
GraphRAG & Multi-Source Knowledge (SQL, APIs & Real-Time Feeds)
Demonstration | Learning Objective | Required Tools
Demo 1: PDF Question Answering
Learning Objective: Answer questions from uploaded documents with citations.
Required Tools: FAISS/Chroma DB
Demo 2: Enterprise Knowledge Assistant
Learning Objective: Build an internal knowledge chatbot.
Required Tools: RAG
Demo 3: SQL Analytics Agent
Learning Objective: Convert natural language questions into SQL queries.
Required Tools: LangChain
LangGraph: StateGraph, Nodes, Conditional Routing & Checkpointing
Multi-Agent Orchestration with LangGraph & CrewAI
Collaborative AI Agents: Handoffs, Communication & Recovery
Adaptive AI Agents: Planning, Reflection & Autonomous Reasoning
Demonstration | Learning Objective | Required Tools
Demo 1: Research Team
Learning Objective: Searcher, summarizer, and report writer collaborate.
Required Tools: CrewAI
Demo 2: Software Engineering Team
Learning Objective: Planner, coder, tester, and reviewer solve a coding task.
Required Tools: LangGraph
Demo 3: Customer Support Workflow
Learning Objective: Specialized agents coordinate issue resolution.
Required Tools: CrewAI
AI TRiSM Framework for Agentic & Autonomous Systems
Components of AI TRiSM (Model Risk, Hallucination Quantification & Calibration)
Agentic Security (Data Poisoning & Model Inversion)
Responsible Deployment, Explain ability & Guardrail Architecture
Demonstration | Learning Objective | Required Tools
Demo 1: Prompt Injection Detector
Learning Objective: Identify and block malicious prompts.
Required Tools: Guardrails
Demo 2: Hallucination Evaluation
Learning Objective: Assess factual grounding of model responses.
Required Tools: DeepEval
Demo 3: PII Protection
Learning Objective: Detect and mask sensitive information before model invocation.
Required Tools: Presidio
Agent APIs with FastAPI & Enterprise Service Exposure
Lang Smith: Tracing, Debugging Multi-Step Failures & Agent Run Replay
Async API Design, Streaming Responses & WebSocket Support
Demonstration | Learning Objective | Required Tools
Demo 1: FastAPI Agent Service
Learning Objective: Expose an AI agent through REST APIs.
Required Tools: FastAPI
Demo 2: Web Interface
Learning Objective: Deploy an interactive assistant with Streamlit.
Required Tools: Streamlit
Demo 3: Docker Deployment
Learning Objective: Package the application for portable deployment.
Required Tools: Docker
LangGraph Cloud Deployment, Stateful Session Management & Scaling
CI/CD for LLM Apps (Prompt Versioning, A/B Testing & Staged Rollouts)
Agentic AI Economics: Cost Optimization, Token Budgeting & ROI Measurement
Demonstration | Learning Objective | Required Tools
Demo 1: LangSmith Tracing and Debugging
Learning Objective: Trace an LLM application and inspect prompts, responses, tool calls, execution steps, token usage, latency, and errors.
Required Tools: LangSmith
Demo 2: Token Cost and Latency Dashboard
Learning Objective: Collect and visualize token consumption, estimated cost, request volume, errors, and response latency.
Required Tools: Streamlit + LangSmith or LiteLLM telemetry
Demo 3: Multi-Model Routing and Failover
Learning Objective: Route requests according to task complexity, cost, latency, and availability; demonstrate retries and model fallbacks.
Required Tools: LiteLLM
Prompt engineering challenge on a business dataset
Build and test a single autonomous AI agent
Implement a RAG pipeline for enterprise document Q&A
Design a multi-agent workflow using LangGraph or CrewAI
Apply context engineering techniques to optimize LLM-based applications
Beginner Projects
AI Email Productivity AssistantBuild an assistant that summarizes emails, drafts context-aware replies, and requests approval before sending.
Meeting Intelligence AssistantConvert meeting transcripts into summaries, action items, and follow-up tasks.
Resume Review AssistantEvaluate resumes against job descriptions and recommend targeted improvements.
Enterprise Document Q&ACreate a retrieval-augmented assistant that answers questions from uploaded documents with citations.
Natural Language SQL AnalystTranslate business questions into SQL queries and visualize the retrieved data.
11. Capstone ProjectBuild an end-to-end Agentic AI application incorporating AI TRiSM principles applications
