Practical AI
Artificial intelligence, explained through real work
A practical guide to understanding AI, choosing useful tasks, evaluating results, and turning a promising experiment into a repeatable workflow.
AI field notes
Learn how to use AI, build useful agents, choose the right model, and improve workflows without adding unnecessary complexity.
Start with a topic
Each permanent topic hub connects foundational explanations, focused guides, and tools you can use immediately.
Practical AI
A practical guide to understanding AI, choosing useful tasks, evaluating results, and turning a promising experiment into a repeatable workflow.
ChatGPT
Learn how to use ChatGPT with better context, reusable instructions, evaluation steps, and workflows that hold up beyond a single conversation.
Claude
A practical path for using Claude in writing, analysis, coding, Claude Code, Skills, and MCP workflows without losing control of the result.
AI Agents
Learn what AI agents are, when they are useful, how to control permissions and cost, and how to move from a safe pilot to a monitored workflow.
AI Automation
A practical guide to finding automation opportunities, mapping a workflow, keeping human checkpoints, and measuring whether the system saves useful time.
AI for Business
Learn how to choose practical AI use cases, scope a first project, manage risk, and measure results without buying an oversized stack.
Latest posts
Build a controlled n8n workflow that delivers an Instagram lead magnet after a keyword comment, captures the lead, and hands complex conversations to a human.
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A practical workflow for turning one recurring client problem into a fixed-scope, AI-assisted service with human review and measurable value.
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Stop treating Claude like a search box. Build a reusable Project that turns trusted information into reviewed, editable work you can use again.
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AI suppliers are generating substantial revenue, but many corporate buyers still cannot connect their growing AI costs to earnings. Here is how to manage AI as a finance portfolio instead of a collection of technology projects.
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A practical, vendor-neutral workflow for building an AI agent with clear boundaries, tested tools, approval gates, complete traces, and measurable results.
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Stop starting from a blank chat. Build a source-to-deliverable workflow that gives ChatGPT approved context, clear constraints, and a human review step.
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Use a four-factor scorecard to decide when AI can draft a client email, when it needs substantive human review, and when it is off-limits.
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Build a cited, human-approved renewal decision packet from contracts, invoices, usage, and vendor performance evidence before an auto-renewal deadline passes.
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A practical 30-day workflow for reviewing AI vendor renewals across a messy stack of 25 tools, with clear keep, renegotiate, replace, restrict, or cancel decisions.
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A practical five-gate workflow for using AI to catch omissions, unclear language, tone issues, and risky commitments before a client email or proposal is sent.
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Turn a weekly expert voice note into reliable social posts, newsletter sections, and video scripts by separating transcription, verification, and adaptation.
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A practical weekly workflow for turning approved meeting notes, emails, and project evidence into a credible article, social post, email, and FAQ without handing AI your entire workday.
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Turn approved meeting notes, emails, and project documents into a review-ready weekly content package without handing AI your entire workday.
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A controlled AI content repurposing workflow that turns one approved asset into useful channel drafts without unsupported claims or off-brand copy.
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A practical workflow for using AI to handle invoice and expense exceptions without handing it approval authority.
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A practical two-pass AI workflow for turning client call transcripts into a traceable scope draft, explicit approval request, and defensible project baseline.
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Turn recurring operational feedback into controlled, reviewable SOP change proposals with AI handling the synthesis and humans retaining approval.
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A practical way to use 10 AI tools without handing over your judgment: draft, verify, approve, then act.
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A practical five-step ChatGPT workflow for professionals and creators: better briefs, grounded drafts, verification, and clear human review thresholds.
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A practical way to route AI work using task impact and measured uncertainty, so you can automate safely without reviewing every output.
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Stop treating an AI confidence score as permission to automate. Use a three-lane release gate that routes work to auto-send, review, or retry.
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A practical beginner method for using AI without getting lost in tools: choose one recurring task, prompt clearly, and review every result.
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Skip the fantasy of ten autonomous AI employees. Use this progressive-delegation framework to choose, pilot, and measure one approval-gated workflow first.
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Stop calling every saved minute AI ROI. Run a 30-day test on one workflow to measure adoption, quality, captured capacity, full cost, and net value.
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Agent credits disappear quickly when you measure runs instead of useful results. Learn how to track fully loaded cost per accepted outcome, set task budgets, and cut waste without sacrificing quality.
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Stop wasting agent credits on repeated context, oversized tool outputs, and runaway loops. Audit one workflow, set limits, and measure cost per successful task.
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A practical, read-only AI workflow that turns scattered work signals into a Monday plan without handing over control.
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Build a practical first AI agent that turns messy feedback into a structured briefing, then routes every recommendation through human review.
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Stop stitching together disconnected AI experiments. Use one simple loop to turn recurring inputs into reviewed work and low-risk handoffs.
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A practical, low-risk workflow for using a Vapi AI receptionist after hours: capture leads, handle basic questions, escalate urgent calls, and create reliable follow-up records.
Read the post →Stop trying to make one chatbot research, analyse documents and create visuals. These three free AI tools handle those jobs better.
Read the post →Stop collecting AI apps. Use a practical three-tool workflow to discover sources, build an evidence pack, and produce work you can review and use.
Read the post →Public LLM leaderboards are fragmented, gamed, and unstable in 2026. Here's a 30-minute workflow to build a private test set and pick the right model for your actual work.
Read the post →Skills and MCP servers solve different problems, but the names make them sound interchangeable. Here's a plain breakdown of what each one actually does, and a flowchart to pick the right one in under a minute.
Read the post →Claude Code is Anthropic's terminal-based coding agent — here's how to install it, actually use it well, and where it falls short in real projects.
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