AI-TUTORIALS

Expert analysis by André Dias Moreira Prol on when to use Fine-tuning versus Retrieval-Augmented Generation (RAG) for enterprise AI, focusing on cost, auditability, and technical implementation.

Learn how to significantly reduce latency and costs in your Generative AI applications using Amazon Bedrock's prompt caching for Claude 4.6. This guide covers the underlying KV cache architecture, implementation strategies with Python, and performance optimization tips.

Claude Opus 5 has arrived, setting new benchmarks for agentic AI and coding while significantly reducing the cost per task compared to Claude Fable 5. Learn how it stacks up against GPT-5.6 Sol.

The AI ecosystem is mirroring the 2015 container wars. As open-weight models like Llama, Qwen, and DeepSeek-V3 reach frontier-level performance, the center of gravity is shifting from proprietary APIs to an open, customizable substrate. Learn why developers must adapt now.

Standard context windows often discard critical long-term information in favor of recent noise. This tutorial explores building a memory engine based on the Ebbinghaus forgetting curve to optimize AI agent performance.

A deep dive into the Model Context Protocol (MCP) July 2026 revision, focusing on stateless architecture, transport layers, and building interactive UIs that survive protocol shifts.

A deep dive into Loop Engineering for RAG, featuring a performance sweep of 20 local models against hosted flagships and strategies for cost-effective escalation.

Most AI agent tutorials suggest vector databases for memory, but for specific facts and shared state, a structured key-value approach is often more reliable and easier to maintain.

A deep dive into how Model Context Protocol (MCP) tool descriptions can be used to hijack LLM agents and how to build a static scanner to prevent behavioral overrides.

Discover why most RAG failures are extraction errors rather than true hallucinations, and learn how to implement a typed generation contract using seven key architectural patterns to ensure enterprise-grade document intelligence.

Discover why AI engineers are shifting from complex vector databases to sqlite-vec for building autonomous agents. Learn how to implement high-performance, local-first RAG memory with zero external dependencies.

Learn how to implement GitOps for AI agents to version control memory states, manage tool manifests, and execute surgical rollbacks of catastrophic learning artifacts.