Build•21 min read•Advanced
The Hidden Memory Layer Behind Long-Context AI
Why inference context memory, not GPU compute, is the next AI bottleneck. A practitioner's guide to the KV cache, prefix caching, memory tiers, and governance.
Ritesh Vajariya
AI Guru® Insights
Practical AI Guides, Frameworks & Strategy
Practical insights, governance frameworks, and career guidance for professionals navigating the AI-first era.
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Latest·Build·Intermediate
Andrew Ng's 3 loops — and the 4th loop enterprise teams keep skipping
Andrew Ng's three loops for agentic coding are strong. Regulated enterprises need a fourth — the governance loop. Here's what Loop 4 looks like and why.
Ritesh Vajariya·June 30, 2026·6 min read
Build•16 min read•Beginner
The Gigawatt Era: What AI's Power Unit Really Means
One gigawatt is a billion watts - the unit now defining AI data centers from Meta to OpenAI's Stargate, and why the world may need over 200 of them by 2030.
Ritesh Vajariya
Build•22 min read•Intermediate
Disaggregated Inference Explained: How LLM Serving Is Splitting Apart
A primer on disaggregated inference: how separating prefill and decode reshapes LLM serving, why the KV cache matters, and when this architecture pays off.
Ritesh Vajariya
Editor's Picks
AI Glossary
From Flash Attention to RAG — the definitive dictionary for AI, ML, and Governance terminology.
Train•7 min read
Best Enterprise AI Training in the US — A 2026 Comparison
Honest 2026 guide to US enterprise AI training across academic incumbents, skills platforms, and practitioners - how to choose by fit, not just brand prestige.
AI Guru Team
Govern•6 min read
Third-Party AI Risk — How to Govern What You Don't Build
Most AI risk lives in vendor and SaaS tools you didn't build - manage it through procurement, vendor assessment, contracts, and ongoing monitoring controls.
AI Guru Team
Govern•6 min read
ISO 42001 — The First Certifiable AI Governance Standard
ISO 42001 is the first AI management standard you can certify against - how auditing works, and how it complements NIST AI RMF and EU AI Act compliance.
AI Guru Team
Build•5 min read
AI Data Governance — Training Data, Quality, Bias, and Documentation
Governing AI training data in practice - consent and legality, quality dimensions, sources of bias, and cross-border challenges, grounded in NIST and EU rules.
AI Guru Team
Govern•4 min read
AI Decommissioning — When and How to Shut Down an AI System
When to retire an AI system - regulatory shifts, performance decay, stakeholder opposition - plus a checklist for shutdown, transition, and notification.
AI Guru Team
Govern•6 min read
The EU AI Act — A Practitioner's Guide to Risk Classification
A practitioner's walkthrough of the EU AI Act's four risk tiers, GPAI rules, and penalties up to 7% of global turnover - plus what the timeline means.
AI Guru Team
Train•4 min read
AI Training & Awareness — Building a Governance Culture
Policies without understanding produce compliance theater - how tiered AI literacy training, from board to staff, makes governance culture truly take root.
AI Guru Team
Build•4 min read
Model Cards, Datasheets, and AI Documentation — What You Need and Why
What model cards and dataset datasheets should document - intended use, limitations, metrics, ethics - and how to match documentation rigor to system risk.
AI Guru Team
Govern•5 min read
Agentic AI Governance — When Your AI Can Take Actions
When AI acts instead of advising, a governance failure becomes a bad outcome. Covers authority delegation, scope limits, oversight, and incident response.
AI Guru Team
Govern•4 min read
AI and Consumer Protection Law — Unfair and Deceptive Practices
How FTC Act Section 5 applies to AI - what makes a practice unfair or deceptive, enforcement actions, and emerging risks like AI-personalized dark patterns.
AI Guru Team
Govern•5 min read
Building an AI Governance Program — Roles & Structure
AI governance is an operating model, not a document - the roles, teams, and structure to scale oversight from 5 models to 500 without becoming a bottleneck.
AI Guru Team
Build•5 min read
AI Testing and Validation — Beyond Accuracy Metrics
A model can be 95% accurate overall yet 60% for one demographic - why testing needs the TEVV framework, disaggregated evaluation, and fairness analysis.
AI Guru Team