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Guided reading library

Articles

Read the clearest practical guides without browsing everything at once. Pick a path, then move from concept to workflow to safer decisions.

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Choose a reading path

Start with the track closest to your work today. Each path turns a large article library into a deliberate sequence.

Foundation

Build confidence with AI basics, prompting, privacy, hallucinations, and everyday use.

Understand what AI can and cannot do before you automate anything.

20 articles125 min total

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Recently reviewed

Articles recently checked for accuracy, usefulness, and fit with the AI Expert learning paths.

Practitioner

Learn workflows for meetings, writing, research, no-code tools, and repeatable business tasks.

Turn AI from a chat box into a dependable work habit.

22 articles169 min total

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1The anatomy of a prompt: role, context, task, constraints, format8 min read
2Stop prompting once: the iterative conversation method8 min read
3Picking the right model for the job: a 2026 decision cheat sheet7 min read

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Builder

Go deeper into agents, RAG, MCP, structured outputs, evals, APIs, and local AI.

Evaluate and build AI systems without treating demos as production.

43 articles483 min total

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1Building reusable prompt libraries: from snippets to shared templates11 min read
2Multi-tool workflows: combining ChatGPT, Claude, Perplexity, and Notion11 min read
3Chain-of-thought, self-critique, tree-of-thoughts — when to use each10 min read

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Strategic

Cover governance, EU AI Act readiness, build-vs-buy decisions, ROI, and private AI choices.

Make safer AI adoption decisions for a team or company.

11 articles109 min total

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1Designing a team AI adoption playbook11 min read
2The AI marketing stack: content, SEO, social on autopilot10 min read
3The AI sales stack: lead enrichment, personalization, follow-up at scale11 min read

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1What AI actually is (and isn't): a no-hype primer6 min read

2Your first hour with ChatGPT: a click-by-click walkthrough6 min read

3The seven things you can ask ChatGPT on day one6 min read

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9 min read

AI ROI and maturity: how to measure adoption that actually works

AI adoption should not be measured by how many people tried ChatGPT. A practical framework for measuring workflow ROI, quality, risk, maturity, and scale-readiness.

Measure AI adoption using workflow ROI, quality, risk controls, and maturity levels instead of tool usage vanity metrics.

AdvancedAI for Business

9 min read

Build vs buy AI systems: the practical decision framework

Most teams should buy before they build, but not always. A decision framework for AI tooling, workflow automation, RAG, agents, privacy, integration depth, total cost, and strategic differentiation.

Decide when to buy, configure, extend, or build an AI system based on workflow fit, data control, cost, capability, and strategic value.

AdvancedAI for Business

9 min read

AI-native IDEs and repository-aware coding workflows

Cursor, Copilot, Claude Code, and repository-aware agents change software work only when teams add boundaries. A practical workflow for codebase context, planning, tests, review, secrets, and production safety.

Design a repository-aware AI coding workflow that improves delivery speed without weakening review, security, tests, or ownership.

AdvancedAI for Business

13 min read

Shipping an LLM product: pricing, margins, and the anti-moat trap

LLM-powered products face economics that are harder than traditional SaaS. Variable costs that scale with usage, margins squeezed by inference, commoditization risk, and competitors with the same foundation models. How to build a product that's actually defensible — and the patterns that lead to LLM

Use the article as decision context for adoption, risk, governance, or investment choices.

AdvancedAI for Business

12 min read

Cost-optimizing inference: prompt caching, routing, and output control

LLM inference costs are 60-90% reducible with the right techniques. Prompt caching, model routing, output control, batching, and a few less-known patterns. The numbers, the patterns, and the production discipline that distinguishes well-run inference from a runaway bill.

Use the article as decision context for adoption, risk, governance, or investment choices.

AdvancedAI for Business

12 min read

Choosing between prompting, RAG, and fine-tuning (and when to combine)

Prompting, RAG, and fine-tuning are the three big levers for adapting LLMs to your problem. Each is right for some problems and wrong for others. A framework for choosing, the realistic costs of each, and the production patterns where combining them shines.

Use the article as decision context for adoption, risk, governance, or investment choices.

AdvancedAI for Business

10 min read

Evals for non-engineers: know if your AI workflow is getting better or worse

Evals — systematic measurement of AI output quality — are usually treated as an engineering concern. But every team running AI workflows needs them, and the basics are accessible without code. The how-to.

Measure whether an AI workflow is improving by using examples, rubrics, and regression checks.

IntermediateAI for Business

11 min read

Designing a team AI adoption playbook

Most teams fail at AI adoption not because the technology doesn't work, but because the rollout doesn't. A practical playbook: how to pick use cases, train people, set policy, measure impact, and avoid the common failures.

Create a team adoption plan that covers use cases, training, governance, measurement, and rollout risk.

IntermediateAI for Business

Secure document ingestion for RAG: PDFs, OCR, metadata, and retention

11 min read

Design a secure document-ingestion pipeline for RAG with permission metadata, OCR quality checks, source freshness, retention rules, deletion behavior, and ingestion tests.

AI ROI and maturity: how to measure adoption that actually works

9 min read

Measure AI adoption using workflow ROI, quality, risk controls, and maturity levels instead of tool usage vanity metrics.

Build vs buy AI systems: the practical decision framework

9 min read

Decide when to buy, configure, extend, or build an AI system based on workflow fit, data control, cost, capability, and strategic value.

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