Generative AI
The complete modern GenAI stack — how LLMs work, prompt engineering, embeddings, vector databases, RAG, LangChain, fine-tuning, multimodal models, agents and production GenAI with MCP.
https://github.com/waleedkhokar/python-ai-engineering-journey/tree/main/05%20Generative-AI — so you always land in the right place, never on a guessed sub-folder.What You Will Learn
All Classes
Every class in this course, in order — with exactly what each class teaches. Notes, code and interview prep for each class live in the course folder on GitHub.
Setup & Environment
Python, API keys, environment variables, Git, notebooks, package management.
GenAI Fundamentals
Generative models, use cases, AI vs ML vs DL vs GenAI, limitations.
LLM Fundamentals
Tokens, tokenization, context windows, transformer recap, temperature, sampling, hallucinations.
LLM APIs & Providers
OpenAI, Claude, Gemini — messages, roles, streaming, retries, model selection.
Prompt Engineering
Instructions, few-shot prompting, context, templates, decomposition, prompt-injection awareness.
Structured Outputs
JSON schema, Pydantic, structured responses, validation, parsing failures.
Embeddings & Semantic Search
Vector representations, cosine similarity, embedding models, chunking.
Vector Databases
FAISS, Chroma, pgvector, metadata filtering, hybrid search.
RAG Fundamentals
Document ingestion, PDF loading, chunking, retrieval, citations.
Advanced RAG
Query rewriting, metadata-aware retrieval, reranking, hybrid search, parent-child chunks, RAG evaluation.
LangChain
Components, model interfaces, prompts, output parsers, chains, loaders, retrievers.
Tool Calling & External APIs
Function schemas, external APIs, database queries, validation, error handling.
LLM Evaluation & Guardrails
Test datasets, correctness, groundedness, regression testing, output constraints, safety.
Fine-Tuning & Open Models
Prompting vs RAG vs fine-tuning, datasets, LoRA / QLoRA, evaluation.
Multimodal GenAI
Vision-language models, image understanding, audio, speech, multimodal inputs.
AI Agents Fundamentals
Model + tools + state, agent loops, stop conditions, failure handling.
Agentic AI & Workflows
ReAct, planning, routing, parallel workflows, reflection, deterministic vs agentic.
Agent Memory & State
Conversation history, short-term and long-term memory, persistence, checkpoints.
Multi-Agent Systems
Supervisor patterns, handoffs, shared state, delegation, coordination.
Production GenAI
FastAPI, async, streaming, Docker, auth, caching, cost, tracing, monitoring.
Advanced Protocols & Integration (MCP)
MCP clients and servers, resources, prompts, tools, authentication, approval workflows.
Projects in this course
Build them all — each project lives in the course folder on GitHub.
Interview Preparation
Every class in this course includes interview preparation. Open the course folder on GitHub for the full material.
Common Interview Questions
The questions interviewers actually ask on this course's topics, with clear, structured answers.
Open Interview PrepCoding Challenges
Hands-on problems for each class, so you can prove the skill, not just describe it.
Open Interview PrepCommon Mistakes to Avoid
The errors beginners make with these topics — and how professionals avoid them.
Open Interview PrepBest Practices
How working engineers write this code in production: clean, tested and maintainable.
Open Interview Prep