zai-ai

name	zai-ai
description	Comprehensive guide to Artificial Intelligence basics, including LLMs, Machine Learning, and Generative AI principles.

Language and Coding Standards

• Communication: Always talk in Thai when interacting with users.
• Code & Technical Assets: All code, comments, documentation, and technical definitions must be in English.

AI Fundamentals Skill

ZeaZ Platform & apps/* Monorepo Rules

When implementing tasks on the zeaz-platform repository, you MUST strictly enforce these architecture and workflow rules:

1. Monorepo Architecture (apps/*): The platform is a unified monorepo. ALL applications, microservices, frontends, and AI toolings (e.g., zLinebot, zwallet, zdash) reside inside the apps/ directory. Do not create top-level directories for apps. When refactoring or adding features, always scope your work to the specific apps/<app-name>/ folder.
2. Environment Variables: Avoid scattering .env files. Consolidate environment variables into a central .env.example inside the respective app folder. Canonical Cloudflare variables (e.g. CLOUDFLARE_API_TOKEN, CLOUDFLARE_ZONE_ID) MUST be used instead of legacy CF_ variants.
3. Commit Workflow: NEVER use git commit or git push directly. ALWAYS stage your intended files with git add and commit using make gpg-finalize COMMIT_MSG="..." from the repository root to ensure all GitOps and DevSecOps checks pass.
4. Security: NEVER commit or generate real secrets. Unsafe placeholders like test-secret-value-value-value, test-secret-value-value-value, test-secret-value-value-value are FORBIDDEN.
5. Language: Code, documentation, and technical definitions MUST be in English.

1. Overview

Artificial Intelligence (AI) encompasses various domains including Machine Learning (ML), Deep Learning (DL), and Generative AI. Understanding these fundamentals is crucial for leveraging AI effectively in business and engineering.

2. Core Concepts

• Machine Learning (ML): Algorithms that learn patterns from data rather than being explicitly programmed.
• Deep Learning (DL): A subset of ML using multi-layered artificial neural networks.
• Generative AI: Systems capable of generating text, images, or other media in response to prompts (e.g., LLMs, Diffusion models).
• Large Language Models (LLMs): Massive neural networks trained on vast amounts of text to understand and generate human language.

3. Best Practices

• Understand Limitations: Always account for AI hallucinations and bias. AI models predict the most likely next token, they do not "know" facts inherently.
• Data Quality: The output quality of any AI model is strictly bounded by the quality of its training or context data ("Garbage in, garbage out").
• Iterative Refinement: AI integration is an iterative process. Start with simple models/prompts and add complexity only when necessary.

4. Key Terminology

• Token: The basic unit of data processed by an LLM (roughly 3/4 of a word).
• Context Window: The maximum number of tokens an LLM can process in a single request.
• Parameters: The internal variables learned by the model during training.