Free Live Sessions · AI Engineering AI Engineering
for Software Engineers
No prior ML experience needed. Six modules, 23 topics, real production applications.
💻 Free Live Zoom6 Modules23 Topics
01
Module 01 · Starting Soon
Foundations
(No ML Required)
1
AI Engineering vs. ML Engineering vs. Data Science — clearing up the confusion2
How LLMs actually work — tokens, next-token prediction, training vs. inference (no math)3
Embeddings explained simply — what a vector representation is and why it matters4
Context windows, temperature, and other inference-time knobs
02
Module 02
Talking to Models
5
Calling an LLM API for the first time — your first "Hello World"6
Prompt engineering fundamentals — zero-shot, few-shot, chain-of-thought7
Structured outputs and function calling / tool use8
System prompts vs. user prompts and message roles
03
Module 03
Giving LLMs Knowledge & Memory
9
Why LLMs hallucinate and the limits of pure prompting10
RAG from scratch — the core idea before any framework11
Vector databases — what they are and when you need one (Chroma, Pinecone, etc.)12
Chunking strategies and retrieval quality
04
Module 04
Building Real Applications
13
Agents — what makes something "agentic" vs. a simple chatbot14
Tool use and function calling in practice15
Frameworks overview — LangChain, LangGraph, LlamaIndex (when to use each)16
Multi-step workflows and orchestration
05
Module 05
Making It Production-Ready
17
Evaluation — how do you know if your AI app is actually good?18
Observability and tracing — logging what the model actually did19
Cost and latency optimization20
Guardrails, safety, and handling failure modes21
Deployment basics — from notebook to a real app
06
Module 06
Where ML Knowledge HelpsOptional
22
Fine-tuning vs. RAG vs. prompting — when each approach makes sense23
A gentle intro to ML concepts AI engineers actually need
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