AI Expert

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.

Foundation

Build confidence with AI basics, prompting, privacy, hallucinations, and everyday use.
20 articles125 min total
Preview first articles
  1. 1What AI actually is (and isn't): a no-hype primer6 min read
  2. 2Your first hour with ChatGPT: a click-by-click walkthrough6 min read
  3. 3The seven things you can ask ChatGPT on day one6 min read
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Practitioner

Learn workflows for meetings, writing, research, no-code tools, and repeatable business tasks.
22 articles169 min total
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  1. 1The anatomy of a prompt: role, context, task, constraints, format8 min read
  2. 2Stop prompting once: the iterative conversation method8 min read
  3. 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.
43 articles483 min total
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  1. 1Building reusable prompt libraries: from snippets to shared templates11 min read
  2. 2Multi-tool workflows: combining ChatGPT, Claude, Perplexity, and Notion11 min read
  3. 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.
11 articles109 min total
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  1. 1Designing a team AI adoption playbook11 min read
  2. 2The AI marketing stack: content, SEO, social on autopilot10 min read
  3. 3The AI sales stack: lead enrichment, personalization, follow-up at scale11 min read
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11 results
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Viewing learning path: StrategicShow all

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
10 min read

Private AI deployment patterns: local, VPC, self-hosted, and hybrid

Private AI is not one architecture. A practical comparison of local models, enterprise SaaS, VPC deployments, self-hosted inference, and hybrid patterns for SMEs that care about privacy and control.

Choose a private AI deployment pattern based on data sensitivity, capability needs, cost, latency, and operational capacity.

AdvancedPrivate / Local AI
9 min read

Voice agents for customer flows: where they work and where they fail

Voice agents are useful when the flow is bounded, the data is available, and the fallback is clean. A practical decision framework for Twilio/Retell-style systems, disclosure, handoff, testing, and rollout.

Decide whether a customer voice agent is appropriate and design the first rollout with disclosure, escalation, testing, and monitoring.

AdvancedAutomations
9 min read

EU AI Act for SMEs: a practical governance plan

The EU AI Act is not just a legal problem for large vendors. A practical SME plan for inventory, risk classification, human oversight, transparency, vendor records, and rollout discipline.

Create a practical AI governance baseline for an SME using AI tools, automations, or customer-facing systems in the EU.

AdvancedAI Safety & Data Privacy
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
11 min read

Self-hosted vs hosted inference: vLLM, TGI, and the break-even math

At what scale does self-hosting beat API calls? The actual math, the operational realities, and the patterns that distinguish teams who should self-host from teams who should keep paying for managed inference.

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

AdvancedPrivate / Local AI
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
13 min read

The 2026 LLM stack: models, inference, tooling, and trade-offs

A working architect's view of the 2026 LLM stack — the model tiers, inference providers, orchestration layers, evaluation tooling, and the trade-offs that actually matter when shipping production AI. Everything you wish someone had laid out before you started.

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

AdvancedChatGPT & LLMs

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