How LLMs Work & Why Prompt Engineering Matters скачать в хорошем качестве

How LLMs Work & Why Prompt Engineering Matters

Prompt engineering isn’t about tricks — it’s about understanding how large language models (LLMs) actually work. In this first part of the series, we unpack the evolution of LLMs — from Seq2Seq to Transformers — and explain how models generate responses, why prompting replaced fine-tuning, and what it means to design prompts that shape output reliably. We also touch on the system design behind prompt-driven workflows, memory injection, and multi-modal prompts — setting the stage for building real-world AI applications.This is where solid architecture meets practical AI. My Linkedin Profile:   / bytemonk   📌 Timestamps 0:00 – Intro: Why Prompt Engineering Matters 1:05 – What Is a Language Model Really Doing? 1:46 – Early Models: Seq2Seq and Thought Vectors 2:28 – Bottlenecks in Seq2Seq (and Why It Failed) 3:56 – Attention Mechanism and “Attention Is All You Need” 4:50 – Birth of Transformers (Parallelism, Power, Limitations) 5:39 – Enter GPT: From Fine-Tuning to Prompting 6:49 – Prompts vs Completions: The Heart of LLMs 8:20 – Predicting Continuations with Real-World Patterns 8:32 – Prompt Engineering as a System, Not Just a Prompt 9:04 – Levels of Prompting: Context, Memory, Tools    • System Design Interview Basics      • System Design Questions      • LLM      • Machine Learning Basics      • Microservices      • Emerging Tech   AWS Certification: AWS Certified Cloud Practioner:    • How to Pass AWS Certified Cloud Practition...   AWS Certified Solution Architect Associate:    • How to Pass AWS Certified Solution Archite...   AWS Certified Solution Architect Professional:    • How to Pass AWS Certified Solution Archite...   #PromptEngineering #LLM #GPT4 #agenticai