Large Language Model (LLM) Architecture

Understanding how Large Language Models (LLMs) like GPT, BERT, and T5 think is the modern cornerstone of AI literacy.
Their architecture reveals how machines encode, reason, and generate human-like text — a fusion of mathematical elegance and engineering precision.
If you can truly explain why Transformers replaced RNNs, you’re already thinking like a top-tier ML engineer.

“The goal of learning is to understand, not just to remember.” — Anonymous

ℹ️
Top interviewers use this topic to measure architectural understanding — whether you can connect a model’s design to its performance and scaling behavior.
You’re not just expected to describe how Transformers work, but to reason through design trade-offs like attention cost, sequence modeling, and inductive bias.
In advanced interviews, clarity about why each architectural choice exists — not just what it does — distinguishes strong candidates from surface-level practitioners.
Key Skills You’ll Build by Mastering This Topic
  • Architectural Reasoning: Explain how encoder–decoder structures shape learning dynamics.
  • Mathematical Intuition: Derive and interpret self-attention as a context aggregation process.
  • Scaling Awareness: Understand why LLMs get smarter (and sometimes dumber) as they grow.
  • Comparative Insight: Contrast RNNs, CNNs, and Transformers from efficiency and design perspectives.
  • Articulate Thinking: Communicate complex mechanisms like attention and positional encoding in simple terms.

🚀 Advanced Interview Study Path

After mastering LLM architecture, go beyond surface knowledge — explore how design principles scale into billion-parameter intelligence, how to reason about performance bottlenecks, and how to defend design decisions in real interview discussions.

Roadmap
Follow a structured breakdown of Encoder–Decoder models, attention, and positional encoding for interviews.
Cheatsheet
Quickly recall Transformer equations, scaling insights, and key trade-offs.
Comparisons
Understand when to use encoder-only, decoder-only, or hybrid architectures.
System Design
See how Transformer blocks integrate into real-world LLM systems and pipelines.
Math Concepts
Dive into attention equations, normalization, and gradient stability mathematics.
Coding Snippets
Review minimal, annotated Python implementations of Transformer components.
Interview Questions
Prepare for conceptual and mathematical reasoning questions on attention, scaling, and design choices.
One-Stop Interview Preparation
Get complete, well-explained answers covering architecture, math, and practical trade-offs.

💡 Tip:
In top tech interviews, clarity about how and why attention works — not just code familiarity — determines your depth.
Use this advanced study path to master the logic, trade-offs, and intuition behind Transformer-based architectures, so you can explain design decisions with confidence and insight.