AI topics for builders
A structured path from generative AI fundamentals to production systems, security, and Python development. Each topic combines a mental model, implementation patterns, failure modes, and a practical next step.
Generative AI Fundamentals
Build a clear mental model of the models, tokens, and prompts behind modern generative AI.
5 articles →Retrieval-Augmented Generation (RAG)
Learn how retrieval connects language models to private, current, and domain-specific knowledge.
5 articles →AI Agents & Orchestration
Understand agents as model-driven workflows that plan, call tools, and complete multi-step tasks.
5 articles →Model Context Protocol (MCP)
Learn the concepts and security practices behind connecting models to tools and resources through MCP.
5 articles →Fine-Tuning Models
Learn when and how to adapt a model with examples, efficient training methods, and open models.
5 articles →Local AI & Open-Source Models
Run, optimize, serve, and evaluate open models on your own hardware.
5 articles →Multimodal AI
Explore models and pipelines that understand and generate combinations of text, images, audio, and more.
5 articles →LLMOps (MLOps for LLMs)
Operate LLM applications with reliable prompts, evaluations, cost controls, observability, and deployment practices.
5 articles →AI Security & Guardrails
Protect AI applications from malicious inputs, unsafe outputs, data leaks, and compliance failures.
5 articles →Python for AI Development
Build practical AI applications in Python with clean environments, APIs, data pipelines, and fast prototypes.
5 articles →