LLMs Tutorial

1. LLMs Introduction

What a large language model is, how the transformer architecture works at a high level, and why scale matters.

2. Tokens & Context Windows

What tokens actually are, why context windows are a hard limit, and why longer context costs more compute.

3. How Transformers Work

The self-attention mechanism explained conceptually, encoder vs decoder, and why transformers replaced RNNs.

4. Hallucination & Mitigation

Why LLMs confidently generate false information, and concrete techniques to reduce and catch it in practice.

5. Fine-Tuning vs RAG vs Prompting

A decision framework for adapting an LLM to your problem, with a full trade-off table for each approach.

6. Evaluation & Benchmarking

How LLMs are actually evaluated — perplexity, benchmark suites, human evaluation, and LLM-as-judge.

7. Production Deployment & Cost

Latency and cost trade-offs, caching, batching, and quantization for running LLMs in production.

8. LLM Interview Questions

Commonly asked LLM fundamentals interview questions with clear, practical answers.