ai-engineering-from-scratch

Discover the 4 agent memory architectures taught in AI including hybrid stores tiered blocks virtual contexts and blackboards implemented in Python for production readiness.

Learn how RAG is implemented with token chunking, TF-IDF indexing, and cosine similarity search. Discover our three-stage retrieval architecture and reranking method.

Discover 10 enterprise architectural blueprints for deploying AI agents in production. Learn production patterns for robust, scalable AI services with layered architectures and safety guardrails.

Explore AI agent swarm patterns, the decentralized orchestration for homogeneous agents. Learn how direct state exchange enables efficient parallel task completion for optimization and routing.

Learn to handle multi-agent systems in AI with a production-ready architecture. Explore layered design, state machines, A2A protocols, and failure auditing for robust agent coordination.

Understand the Vision Transformer ViT architecture. Learn how ViT converts images into patch embeddings and uses a transformer encoder for classification. Explore the core concepts.

Learn LLM quantization for inference: Shrink models to INT4/INT8 with GPTQ/AWQ for efficient deployment on limited hardware while preserving accuracy. A complete guide for AI engineers on GitHub.

Learn to implement Direct Preference Optimization DPO for LLM alignment with this comprehensive guide. Optimize language models directly using human preference pairs. No reward model needed.

Learn how to implement RLHF for LLM alignment with this comprehensive guide. Build a reward model and optimize your LLM using PPO from scratch.

Understand the ReAct agent loop: LLM reasons, acts with tools, observes results. Learn this pattern for efficient AI task completion.

Discover the Model Context Protocol (MCP) in AI. Unify LLM tool discovery and execution with this open standard, eliminating integration fragmentation. Learn more.

Master AI Engineering with this 20-phase learning progression. Start with Python and math, advance through ML, DL, LLMs, and tackle multimodal AI and autonomous agents.