Agentic & Generative AI

Python & AI Foundations

• Python syntax and data structures
• Functions and control flow
• Working with requests and JSON
• Setting up virtual environments

Git & Version Control for AI Projects

• Branching and pull requests
• Managing prompts and configs in version control
• Tracking model versions
• Collaborative codebase workflows

Prompt Engineering Fundamentals

• Role-based prompting
• Context windows
• Few-shot examples
• Structured JSON output formatting

Working with LLM APIs

• OpenAI, Anthropic Claude, and Gemini API integration
• Authentication and streaming responses
• Token management and cost control

Embeddings & Vector Databases

• Understanding embeddings and semantic similarity
• Pinecone, ChromaDB, MongoDB Atlas Vector Search

Retrieval-Augmented Generation (RAG)

• Document chunking and embedding storage
• Retrieval pipelines
• Grounding LLM answers in real data

LangChain & LlamaIndex

• Chains and memory
• Retrievers
• Output parsers
• Production LLM app patterns

Building AI Agents

• Tool use and function calling
• Querying databases from an agent
• Taking real actions, not just generating text

Multi-Agent Systems

• Orchestrating agents with LangGraph
• CrewAI and AutoGen fundamentals
• Planning, delegation, and inter-agent communication

Everything in the Starter curriculum, plus:

Fine-Tuning & Model Customization

• When to fine-tune vs. prompt-engineer
• Working with smaller open models
• Cost-efficient, specialized tasks
• Model customization tradeoffs

Frontend Integration for AI Apps

• Connecting an AI backend to React/Next.js
• Streaming responses in the UI
• Building chat interfaces
• Agent status displays

Deployment, Monitoring & AI Ops

• Deploying agent pipelines to the cloud
• Logging agent decisions
• Handling failures gracefully
• Monitoring token spend

Capstone: Production-Ready Agentic AI Application

• Designing a full multi-agent or RAG-based application
• End-to-end build and deployment
• Capstone review and scoring
• Portfolio centerpiece

Interview Preparation

• Agentic AI and LLM interview questions
• Prompt engineering and RAG questions
• Agent architecture and framework questions
• Scenario-based and problem-solving questions
• Project-related questions
• HR interview questions

Mock Interview

• Technical interview (Agentic AI-specific)
• Practical scenarios
• Project discussion
• HR questions
• Interview feedback and improvement areas

Job Assistance

• Job opportunities updates
• Interview preparation support
• Mock interview support
• Resume guidance
• Career guidance
• Job application support