LLM-101: An Interactive Introductory Guide to Large Language Models for Beginners

LLM-101 is an open-source static HTML interactive educational project. It helps beginners understand the core concepts of large language models (LLMs) through 7 concept explanation modules (equipped with visual examples and interactive demos) and LLM-agnostic comparison tabs (Claude/ChatGPT/Gemini). It features zero-dependency deployment, fast loading, and offline availability. Targeted at tech novices, it focuses on establishing correct understanding.

LLM-101 is positioned as an introductory LLM tutorial for "tech novices", using a pure static HTML tech stack without requiring a server environment. Its core design pattern is the "concept interpreter", which breaks down complex AI concepts into 7 modules, avoids mathematical formulas, and lowers the learning threshold through analogies and visualization.

Static Deployment Strategy

Fully static solution where all content exists as HTML/CSS/JS files: zero-dependency deployment (no Node.js/Python required), fast loading (resources hosted locally), offline availability, and long-term maintainability (no reliance on external CDNs/APIs).

LLM-Agnostic Comparison Tabs

Claude/ChatGPT/Gemini tabs are set up to show answer differences to the same question, helping users understand model capability boundaries, develop model selection awareness, and avoid reliance on a single model.

1. What is a Large Language Model: Explains the essence of LLMs (neural networks trained on massive text data) and presents visualizations of pre-training/fine-tuning stages;
2. Tokens and Context Window: Covers token splitting and interactive demos, and introduces context window limitations;
3. Basics of Prompt Engineering: Techniques like zero-shot/few-shot prompting, role setting, and output format control;
4. Model Capabilities and Limitations: Analyzes hallucinations, reliability of mathematical reasoning, long-text degradation, and safety alignment mechanisms;
5. Practical Application Scenarios: Content creation, code writing, learning assistance, multilingual translation;
6. API Call Integration: API key management, request configuration, streaming response handling;
7. AI Ethics: Data privacy, copyright ownership, deepfakes, and employment impact.

Target Audience

• Suitable for: AI enthusiasts who are new to the field, product managers/educators who need to explain LLMs, beginners with basic LLM knowledge;
• Not suitable for: algorithm engineers, production deployment developers.

Educational Value

Provides a structured, low-threshold learning path, focuses on "establishing correct understanding", and helps users avoid information clutter to quickly grasp core concepts.

Performance Optimization

Self-hosted fonts (no Google Fonts dependency): protects privacy, usable in restricted networks, stable styling; font subsetting to reduce size and optimize loading.

Open Source Expansion

A permissive license allows community translation, adding modules, adapting to enterprise internal training, and integrating with educational platforms; the modular architecture makes it easy to expand new content.

LLM-101 is a valuable attempt in AI education, providing a clear learning path for beginners. Although it does not cover all technical details, it focuses on building correct understanding. The static self-hosted solution offers a feasible model for the long-term preservation of educational content, making it a high-quality introductory resource worth saving.