AI & Machine Learning
Prompt engineering, LLM integration, RAG pipelines, and AI-assisted development.
LLM API Integration: Retries, Cost Controls, and Observability
A production guide to integrate LLM providers with retry safety, per-request budget guards, and end-to-end observability.
AI Development Playbook: Multi-Tool, Multi-Repo Architecture for AI Agents
Complete, replicable guide to setting up AI-assisted development with AGENTS.md, rules, skills, progressive disclosure, and the Agent Skills standard. Compatible with Claude Code, Cursor, Copilot, OpenCode, Gemini CLI, and 30+ tools. Validated against academic research.
AI-Assisted Code Reviews: What to Use and What to Avoid
How to use AI during code reviews without lowering technical quality: workflow, guardrails, and limits.
RAG with TypeScript from Scratch: Minimal Architecture that Works
Practical guide to build a lean RAG pipeline in TypeScript with ingestion, embeddings, retrieval, and evaluation.
Prompt Engineering for Developers: Practical Production Guide
A practical prompt engineering framework for software teams: context, constraints, evaluation, and versioning.
PRD + RULES + SKILLS + MCP: Shared Context that Makes AI Actually Useful
Practical strategy for LLM-enabled teams: shared PRDs, versioned rules, role-based skills, and MCP integration.