Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 3 — Server Primitives: Exposing Context and Capabilities
Third post of the LLM Primer IV walkthrough. The three nouns an MCP server can offer — Resources (read state), Prompts (reusable scaffolding), Tools (write actions) — their schemas, their lifecycles, their error models, and the discipline of choosing the right primitive.
2026-04-01
5.2 Compute Resources and Cost
A preview from Chapter 5.2: Learn why LLMs demand massive compute power, what drives cost, and practical strategies to optimize performance and sustainability.
2024-09-30
5.0 Pitfalls & Best Practices When Using LLMs
Discover the hidden risks of large language models—bias, cost, and latency—and learn best practices for deploying LLMs responsibly.
2024-09-28
3.3 Fine-Tuning and Transfer Learning for LLMs: Efficient Techniques Explained
Learn how fine-tuning and transfer learning techniques can adapt pre-trained Large Language Models (LLMs) to specific tasks efficiently, saving time and resources while improving accuracy.
2024-09-14