Practical skills & actionable AI news for engineers
No hype. Every article answers three questions: What changed?, How does it affect real systems?, and What should engineers try next?
AI Skills
Evergreen, production-oriented learning
Build real capability over time: structured paths, operational playbooks, and patterns that hold up in production.
- Learning Paths — 2–6 week tracks (RAG, evals, agents, LLMOps, security)
- Playbooks — step-by-step guides, checklists, reusable templates
- Patterns & anti-patterns — trade-offs, failure modes, when not to use a technique
AI News
News translated into engineering impact
Short briefs for awareness, deeper analysis for decisions, and release digests for implementation.
- Briefs — fast summary + impact + next actions
- Analysis — production trade-offs: cost, latency, reliability, security
- Releases — model/tool updates explained for developers
Why this is different
Production-first
Cost, latency, reliability, security, observability, and operational trade-offs come first.
Evidence-aware
Sources are linked. Assumptions are stated explicitly when evidence is incomplete.
Built for busy engineers
Most posts are readable in minutes and end with clear next steps.
Trade-offs over hype
You’ll see when to use something—and when it’s the wrong tool.
Latest AI News
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Evaluation Is Becoming the Real AI Differentiator
Better models are no longer enough. This article explains why evaluation is emerging as the key differentiator in production AI systems, and how teams that invest in measurement outperform those that rely on intuition.
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Why AI Demos Scale Poorly Into Real Systems
What works in an AI demo often fails in production. This article analyzes the structural gap between demos and real systems, and why reliability, cost, and evaluation become dominant only after scale.
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Why JSON Output Alone Does Not Make AI Safe
JSON schemas help control AI output format, but they do not guarantee correctness or safety. This article explains the limits of structured output and what additional safeguards are required in production systems.
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Chunking Is Still the #1 Bottleneck in RAG
Despite advances in models and embeddings, chunking remains the weakest link in most RAG systems. This article explains why chunking dominates retrieval quality and how poor chunk design quietly undermines production reliability.
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Why Most RAG Systems Fail in Production
RAG promises grounded AI, yet many production systems deliver inconsistent or unreliable results. This article analyzes why RAG fails outside demos and how architectural blind spots—not model quality—are usually responsible.
Latest AI Skills
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Evaluating RAG Quality: Precision, Recall, and Faithfulness
Without evaluation, RAG systems cannot improve reliably. This article introduces practical metrics and evaluation strategies for measuring retrieval accuracy, answer grounding, and regression over time.
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Retrieval Is the Hard Part
Most RAG failures stem from poor retrieval, not weak models. This article explains why retrieval is difficult, how to improve it, and how to debug retrieval failures systematically.
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Chunking Strategies That Actually Work
Effective chunking is an information architecture problem, not a text-splitting task. This article covers practical chunking strategies that improve retrieval accuracy in real-world RAG systems.
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Why RAG Exists (And When Not to Use It)
RAG is not a universal fix for AI correctness. This article explains the real problem RAG addresses, its hidden costs, and how to decide whether retrieval is justified for a given system.
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Prompt Anti-patterns Engineers Fall Into
Many prompt failures come from familiar engineering anti-patterns applied to natural language. This article identifies the most common prompt anti-patterns and explains why they break down in production.