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.

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📌 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  

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