⚠️ Most AI courses teach how to use LLMs.
This module teaches how LLMs actually work under the hood.
In Module 2 of the Agentic AI Masterclass, we take a deep dive into the internal mechanics of Large Language Models (LLMs), embeddings, and prompt engineering—the core building blocks behind modern agentic AI systems.
You’ll clearly understand how:
Tokens, context windows, and attention shape LLM behavior
Embeddings convert text into semantic vector representations
Semantic search and retrieval work in AI systems
Prompt engineering directly influences reasoning, memory, and tool usage
Agentic workflows rely on embeddings and prompts for accuracy and reliability
This module forms the technical backbone for building intelligent AI agents, RAG pipelines, and multi-agent systems.
👉 Part of the Agentic AI Masterclass by PurpleSkill
📌 Who should watch this?
Developers working with LLMs
AI / ML engineers
Prompt engineers
Cloud & solution architects
Anyone building AI agents or RAG systems
⏱️ Chapters :
00:00 – Why Understanding LLM Internals Matters
02:20 – Tokens, Context & Attention Explained
08:40 – How LLMs Generate Responses
14:30 – What Are Embeddings
20:10 – Semantic Search & Vector Similarity
27:50 – Prompt Engineering Fundamentals
34:40 – Prompts for Agentic AI
41:30 – What Comes Next
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Your Queries:
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✅ About PurpleSkill.
Welcome to PurpleSkil! On this channel, you'll find easy-to-understand tutorials and learning videos on AI, Cloud Computing, Python, Machine Learning, and more. I simplify complex tech concepts, making them accessible so you can build practical skills and advance your career.
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Secrets Revealed: Deep Dive into LLMs, Embeddings & Prompt Engineering
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