Generative AI Guide

A skill providing a comprehensive, curated guide to generative AI research and practice, covering large language models (LLMs), diffusion models, transformer architectures, prompt engineering, and evaluation methodologies. Based on the awesome-generative-ai-guide repository (25K stars), this skill equips researchers with structured knowledge of the rapidly evolving generative AI landscape.

Overview

Generative AI has become one of the most active areas of research across computer science, with implications spanning natural language processing, computer vision, audio synthesis, code generation, scientific discovery, and creative applications. The pace of development makes it challenging for researchers to maintain a current understanding of the field. This skill provides a structured map of the generative AI landscape, organized by topic and application area, with guidance on key papers, methods, and practical considerations.

Whether you are an AI researcher staying current with the field, a domain scientist exploring how generative AI can accelerate your work, or a student entering the field, this skill provides the orientation and resources needed to navigate the space effectively.

Large Language Models

Architecture Foundations

• Transformer architecture: self-attention mechanism, positional encoding, layer normalization
• Scaling laws: the relationship between model size, data, compute, and performance
• Training objectives: causal language modeling, masked language modeling, instruction tuning
• Context windows: evolution from 512 tokens to 100K+ tokens and associated techniques
• Mixture of Experts (MoE): sparse activation for efficient scaling

Key Model Families

• GPT series (OpenAI): decoder-only architecture, scaling-driven approach
• Claude series (Anthropic): emphasis on safety, instruction following, and long context
• Llama series (Meta): open-weight models enabling community research
• Gemini series (Google): multimodal from the ground up
• Open-source ecosystem: Mistral, Qwen, DeepSeek, and community fine-tunes