AI Engineering

How production AI systems are built: agents, RAG, MCP, LLM integration, evaluation and guardrails.

Subjects

AI AI Agents Prompt Engineering Enterprise AI RAG Machine Learning LLM MCP Fine-tuning CV

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AI Engineering

31 articles, newest first

Enterprise AI Context Engineering: A Practical Guide Enterprise AI Enterprise AI Context Engineering: A Practical Guide Enterprise AI context engineering connects business knowledge, persistent memory, and authorized data so agents act on current information instead of guesses. Read
Harness Engineering for SMEs: Make AI Coding Agents Reliable Harness Engineering Harness Engineering for SMEs: Make AI Coding Agents Reliable Harness engineering turns AI coding agents from risky code generators into reliable engineering teammates for SMEs with limited teams and high stakes. Read
How to Build an AI Agent in 2026: Architecture, Stack, and Platform Choices AI Agents How to Build an AI Agent in 2026: Architecture, Stack, and Platform Choices A practical framework for how to build an AI agent covering architecture layers, platform choices, and production deployment considerations. Read
Give your agent long-term memory without blowing the context window AI Agents Give your agent long-term memory without blowing the context window A distilled, budget-capped store for coding agents cut an audit session from 8,150 tokens to 564 without changing the answer. Read
Prompt Engineering Is Not Enough. Here Is How to Build Loop Engineering Instead. AI Agents Prompt Engineering Is Not Enough. Here Is How to Build Loop Engineering Instead. Prompt engineering gives you a single response. Loop engineering builds a self-correcting system that improves over time. Read
AI Agent Budgets: How to Stop Runaway Costs Before Go-Live AI Agents AI Agent Budgets: How to Stop Runaway Costs Before Go-Live A practical framework for setting AI agent budgets, guardrails, and termination rules that keep enterprise agent programs cost-controlled and finance-team-friendly. Read
Production AI Agent Loops: Engineering Reliable Systems AI Agents Production AI Agent Loops: Engineering Reliable Systems Production AI agent loops explained: tool calling, verification, context management, budgets, and termination rules that turn demos into reliable enterprise systems. Read
Six AI fraud detection patterns for fintech that SQL rules miss AI Six AI fraud detection patterns for fintech that SQL rules miss SQL rules catch known fraud shapes. AI fraud detection catches adaptive rings, account takeovers, and behavioral drift. Six patterns for fintech teams. Read
9 Essential AI Agent Guardrails [Production Checklist] AI Agents 9 Essential AI Agent Guardrails [Production Checklist] AI agent guardrails prevent unsafe output, prompt injection, and hallucinations in production. Here are the 9 layers you need before deployment. Read
RAG vs Fine-Tuning for Enterprise AI [Decision Framework] RAG RAG vs Fine-Tuning for Enterprise AI [Decision Framework] Compare RAG vs fine-tuning for enterprise AI: costs, speed, and accuracy. A practical guide to help CTOs choose the right AI strategy for their teams. Read
DeepSeek V4 Prompt Engineering: What Actually Works in Production DeepSeek DeepSeek V4 Prompt Engineering: What Actually Works in Production Master DeepSeek V4 prompting with CO-STAR, CoT, and new V4-specific techniques. Includes model routing, dual-mode prompting, and code examples for V4-Pro and V4-Flash. Read
How to Use Claude for Brand-Consistent Mockups: An 8-Step Workflow Claude How to Use Claude for Brand-Consistent Mockups: An 8-Step Workflow Turn brand rules into reusable mockups faster with a repeatable Claude workflow that eliminates generic AI output and messy handoffs. Read