Uploaded May 2026 | Updated September 2026, 2 hours ago
Become an Agentic AI Engineer who designs production-ready systems: academy.towardsai.net/courses/agent-engineering?ref=1f9b29
Our FREE Agentic AI Engineering Guide. A 6-day email course: email-course.towardsai.net
The transition from a traditional software developer to an AI engineer isn't just about learning new tools—it's about mastering a new architectural mindset. In this video, we break down the fundamental pillars of AI engineering that will make you more valuable in today’s market. We move beyond the hype to explore how Large Language Models (LLMs) actually function and, more importantly, how you can build robust, production-ready systems around them.
Whether you are a full-stack developer looking to upskill or an ML engineer moving closer to the product side, this guide provides the foundational knowledge you need to start building agentic workflows, implementing advanced retrieval, and securing your AI applications.
Chapters
00:00 - Introduction: The Rise of the AI Engineer
01:56 - What is AI Engineering? (Role & Core Competencies)
04:33 - How LLMs Actually Work: Data, Tokens, & Embeddings
09:41 - The Transformer Architecture: Attention & Feed-Forward Layers
12:06 - Training Phases: Pre-training, Fine-Tuning, & RLHF
17:18 - Understanding the Real Limitations of LLMs
24:27 - Picking the Right Pattern: From Prompts to Agents
28:19 - Implementing RAG vs. Context Caching
31:11 - Building Reliable Agentic Loops & Workflows
33:18 - Managing AI Memory: Working, Episodic, & Profile Layers
38:09 - When (and When Not) to Fine-Tune Your Models
42:33 - Evaluation Strategies: Moving Beyond "Vibe Checks"
47:11 - AI Security: Protecting Against Prompt Injections
54:15 - Key Takeaways & How to Start Building
#ai #agents #agenticai
Become an Agentic AI Engineer who designs production-ready systems: academy.towardsai.net/courses/agent-engineering?ref=1f9b29
Our FREE Agentic AI Engineering Guide. A 6-day email course: email-course.towardsai.net
The transition from a traditional software developer to an AI engineer isn't just about learning new tools—it's about mastering a new architectural mindset. In this video, we break down the fundamental pillars of AI engineering that will make you more valuable in today’s market. We move beyond the hype to explore how Large Language Models (LLMs) actually function and, more importantly, how you can build robust, production-ready systems around them.
Whether you are a full-stack developer looking to upskill or an ML engineer moving closer to the product side, this guide provides the foundational knowledge you need to start building agentic workflows, implementing advanced retrieval, and securing your AI applications.
Chapters
00:00 - Introduction: The Rise of the AI Engineer
01:56 - What is AI Engineering? (Role & Core Competencies)
04:33 - How LLMs Actually Work: Data, Tokens, & Embeddings
09:41 - The Transformer Architecture: Attention & Feed-Forward Layers
12:06 - Training Phases: Pre-training, Fine-Tuning, & RLHF
17:18 - Understanding the Real Limitations of LLMs
24:27 - Picking the Right Pattern: From Prompts to Agents
28:19 - Implementing RAG vs. Context Caching
31:11 - Building Reliable Agentic Loops & Workflows
33:18 - Managing AI Memory: Working, Episodic, & Profile Layers
38:09 - When (and When Not) to Fine-Tune Your Models
42:33 - Evaluation Strategies: Moving Beyond "Vibe Checks"
47:11 - AI Security: Protecting Against Prompt Injections
54:15 - Key Takeaways & How to Start Building
#ai #agents #agenticai
