AI Engineering Full Course: RAG, Agents, Fine-Tuning, LLMOps

Most LLM courses stop at calling the OpenAI API — then your JSON breaks, your RAG pulls the wrong document, and your agent fails silently in production. This course covers what comes after: structured output and prompt injection defences, OpenAI, Claude, and Gemini APIs, embeddings and vector databases, RAG from naive to GraphRAG, LangChain and LangGraph agents, MCP, fine-tuning with LoRA, open-source models with Ollama and vLLM, LLMOps, multimodal AI, and responsible deployment.

⏱️ Chapters:
0:00:00 The AI Engineering Landscape in 2026
0:06:14 How Large Language Models Work (No Maths Required)
0:12:12 The LLM Ecosystem — Frontier vs Open Source
0:16:46 Development Environment Setup
0:21:15 Cost Management & Token Economics
0:25:56 The Anatomy of a Prompt
0:31:33 Advanced Prompting Techniques
0:37:52 Structured Output — Getting JSON Every Time
0:41:43 Prompt Injection & Security
0:45:22 Prompt Testing & Evaluation
0:48:07 Prompt Versioning & Management
0:51:52 OpenAI API Deep Dive
0:58:29 Anthropic Claude API
1:03:20 Google Gemini API
1:08:31 Building a Unified LLM Interface
1:13:49 Async and Concurrent API Calls
1:17:04 Response Caching & Cost Reduction
1:20:22 Logging, Observability & Debugging
1:23:50 What Are Embeddings?
1:29:15 Similarity Search Deep Dive
1:33:13 Vector Database Landscape
1:36:40 ChromaDB Hands-On
1:39:04 Pinecone for Production
1:41:34 Chunking Strategies
1:45:56 Why RAG? The Problem It Solves
1:49:21 Naive RAG — The Basic Pipeline
1:52:47 Advanced Retrieval Techniques
1:56:31 RAG Evaluation — Measuring What Matters
1:59:08 Hybrid Search RAG
2:02:33 Corrective RAG & Self-RAG
2:05:07 GraphRAG — Knowledge Graph Enhanced Retrieval
2:08:35 Production RAG Architecture
2:09:25 LangChain Architecture & Philosophy
2:13:17 Chains & LCEL
2:16:49 Memory Systems
2:19:49 Document Loaders & Text Splitters
2:22:22 LangChain Retrievers
2:24:57 Output Parsers & Structured Data
2:27:31 LangGraph Introduction
2:31:34 What Are AI Agents?
2:35:45 Tool Use & Function Calling
2:39:04 LangChain Agents
2:41:12 LangGraph for Agentic Workflows
2:43:18 Multi-Agent Systems with CrewAI
2:46:14 Multi-Agent Systems with AutoGen
2:48:13 MCP — Model Context Protocol
2:50:33 Agent Reliability & Production Patterns
2:53:22 When to Fine-Tune (And When NOT To)
2:56:19 Preparing Data for Fine-Tuning
2:58:35 OpenAI Fine-Tuning
3:00:37 LoRA & QLoRA
3:03:12 Evaluation & Iteration
3:05:40 Open-Source LLM Landscape
3:08:37 Running LLMs Locally with Ollama
3:11:26 Production Serving with vLLM
3:14:11 Quantisation
3:16:46 Open-Source LLM End to End
3:19:46 What is LLMOps?
3:23:16 LLMOps: Prompt Versioning
3:25:36 LLMOps: Cost Optimization
3:28:06 LLMOps: Guardrails & Safety
3:30:18 Deployment Strategies
3:32:19 Testing LLM Systems
3:34:19 LLMOps Tools
3:35:48 Vision Models & GPT-4o Vision
3:38:57 Building Vision-Powered Applications
3:41:08 Audio: Whisper & Speech-to-Text
3:43:36 Text-to-Speech & Voice Agents
3:45:51 Multimodal RAG
3:48:08 LLM Safety Risks & Threat Model
3:50:00 Guardrails & Content Filtering
3:52:09 Privacy, Compliance & Data Governance
3:54:14 AI Governance & Responsible Deployment

ℹ️ This course is independent and is not affiliated with, endorsed by, or sponsored by OpenAI, Anthropic, Google, LangChain, or any mentioned organization. For educational purposes only. Basic Python knowledge is recommended.

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