A 10-week hands-on cohort covering LLMs, RAG, AI Agents, and Fine-Tuning. Not just videos — you build on our live Jupyter-powered LLM platform.
Go from zero GenAI knowledge to deploying production-grade AI applications. 10 weeks, 8 projects, 1 capstone — everything you need to build with LLMs.
View full curriculum →Transformer internals, self-attention, tokenization, and the full GPT training pipeline — not surface-level tutorials.
RAG pipelines, multi-agent workflows, fine-tuned models. Every week includes a real-world workshop using live data from APIs like Hacker News, USGS Earthquakes, and GitHub.
Security guardrails, FastAPI deployment, Docker containerization. Ship GenAI apps that are actually safe to deploy.
Most courses give you pre-recorded videos and Colab notebooks. We built a Jupyter-backed environment with note cells, AI prompt cells, and code cells — where AI acts as your always-on instructor. It sees your work, understands your context, and helps you learn in real time.
# AGAI-101 | Week 5: RAG Pipeline
from langchain import ChatOllama
from chromadb import Client
# Load your documents
docs = load_pdfs("./papers/")
chunks = split(docs, chunk_size=512)
# Build the RAG chain
retriever = vectorstore.as_retriever()
chain = retriever | llm | parse
chain.invoke("Summarize the key findings")
# → "The paper presents three main findings..."Each week combines theory, hands-on coding, and a portfolio project. You graduate with 8+ GitHub projects.
Topics: How LLMs work, attention mechanism, model capability ladder, reasoning models, provider landscape, API anatomy, tokens, context windows, temperature, top-p
Project: Model Comparison Tool — same prompt, 3 models, side-by-side with quality assessment
Real-World Workshop: AI News Intelligence Dashboard — live Hacker News data, AI categorization, audience-adapted briefings, token economics
Topics: Zero-shot, few-shot, chain-of-thought, prompt mechanics, constrained decoding, multi-provider structured output, prompt evaluation
Project: Customer Review Analyzer — raw reviews to structured JSON with multi-provider comparison and evaluation metrics
Real-World Workshop: AI Recipe Transformer — live TheMealDB data, dietary adaptation, cuisine fusion, CoT meal planning
Topics: Contrastive learning, MTEB benchmark, embeddings, semantic search, ChromaDB, ANN/HNSW, RAG pipeline, chunking, principled K selection, distance thresholding
Project: Company Knowledge Base Bot — RAG with confidence-aware retrieval and distance thresholding
Real-World Workshop: AI Book Recommendation Engine — Open Library data, semantic search over real books, metadata filtering
Topics: Pipeline pattern, pure-Python RAG, document loaders, chunking strategies, retrieval failure taxonomy, hybrid search (BM25 + semantic), distance thresholding, conversational RAG
Project: Document Q&A System — multi-document RAG with citations, hybrid search, threshold calibration, failure diagnosis
Real-World Workshop: AI Country Intelligence Briefing — REST Countries + Open-Meteo, multi-source RAG, conversational retrieval
Topics: RAGAS metrics, LLM-as-Judge, judge validation (Cohen's kappa), statistical rigor (confidence intervals), cost/latency tracking, query expansion, HyDE, debugging RAG failures
Project: RAG Evaluation Pipeline — baseline scores, optimization experiments, judge validation, statistical rigor, cost/latency tracking
Real-World Workshop: AI Earthquake Analysis — live USGS seismic data, eval dataset creation, retrieval metrics, statistical rigor
Topics: Agents vs chatbots, function calling (OpenAI + Anthropic), multi-provider abstraction, agent loop, tool design principles, agent safety (cost budgets, loop detection), error handling
Project: Personal Assistant Agent — 6 tools, cost tracking, safety guards, multi-provider comparison
Real-World Workshop: AI Space Tracker Agent — live ISS + Sunrise-Sunset + weather APIs, real-time tool calling, multi-tool agent
Topics: Graph abstraction, pure Python executor, LangGraph, state management, conditional routing, self-reflection (Reflexion), graph anti-patterns, human-in-the-loop, agent memory
Project: Content Pipeline Agent — research, draft, review cycles with self-reflection and graph design patterns
Real-World Workshop: AI Content Publishing Pipeline — Wikipedia + HN + Quotable, LangGraph editorial workflow, self-reflection
Topics: Multi-agent architectures, coordination strategies (supervisor/debate/consensus), MCP protocol mechanics (JSON-RPC), multi-agent failure modes, agent safety guards
Project: Multi-Agent NovaTech System — supervisor + specialized workers + MCP tool server with failure recovery
Real-World Workshop: AI OSINT Intelligence Team — GitHub + HN + Wikipedia APIs, multi-agent intelligence, MCP server
Topics: Fine-tuning pitfalls (catastrophic forgetting, cost-benefit), LoRA mechanics (low-rank decomposition, rank selection), QLoRA, Unsloth, alignment as security (RLHF, Constitutional AI), guardrails frameworks, prompt injection defense, OWASP Top 10 for LLMs
Project: Fine-tune a model + add guardrails with forgetting checks, held-out validation, and red team exercise. Capstone kickoff.
Real-World Workshop: AI Security Red Team Lab — real CVE data from NIST, layered defenses, automated red team attacks
Topics: System composition, framework independence (architecture-first thinking), advanced orchestration (self-reflection, parallel, HITL), production deployment (FastAPI, monitoring, observability), Demo Day presentations
Project: NovaTech AI Assistant capstone — self-reflection workflows, framework-independent architecture, production deployment sketch
Real-World Workshop: Build Your Own AI Startup MVP — choose a real problem, combine all course techniques, investor pitch
Sr. Software Engineer & AI Practitioner
10+ years building production AI systems for global enterprises including major FMCG brands like Coca-Cola, Pepsi, GSK, and Sanofi. Former Associate Lecturer at the National University of Singapore, where he trained 750+ professionals and won the Teaching Excellence Award.
You write code daily but want to add GenAI to your skillset. You want to build with LLMs, not just use ChatGPT.
You know classical ML but need to bridge the gap to LLMs, RAG, agents, and fine-tuning.
You need to evaluate and architect GenAI solutions for your organization. Understand what's hype vs what works. Each week includes production-tier challenges.
You have programming fundamentals and want to break into the hottest area of tech with a real portfolio.
~USD 817 · One-time payment
2-3 day intensive · On-site or virtual
Batch 1 starts March 2026. Limited to 25 seats. Get notified when enrollment opens.
No spam. We only email when enrollment opens.