AI for Professionals

The Prompting Handbook

A friendly, hands-on field guide for everyday humans — learn the CRISP framework, spot bad prompts, practice with real recipes, play a drag-and-drop game, and test yourself with a quiz. No code required.

AI for Lawyers

The practical middle ground for legal professionals: where AI genuinely helps (first drafts, brief summaries, review triage), the three non-negotiable rules — confidentiality, verify every citation, you sign it you own it — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the five questions to ask any AI vendor. No coding required.

AI for Doctors

The practical middle ground for clinicians: where AI genuinely helps (notes from dictation, discharge instructions, prior auths, literature summaries), the three non-negotiable rules — PHI needs a BAA, verify everything clinical, the chart is yours — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the six questions to ask any clinical AI vendor. No coding required.

AI for Civil Engineers

The practical middle ground for civil and structural engineers: where AI genuinely helps (observation reports, RFI responses, meeting minutes, plain-language explanations), the three non-negotiable rules — the adopted code is the authority, calculations get checked independently, your stamp is yours — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the vendor questions that matter. No coding required.

AI for Accountants

The practical middle ground for accounting and tax professionals: where AI genuinely helps (first drafts, document summaries, reconciliation triage, plain-language client explanations), the three non-negotiable rules — client-data confidentiality (incl. IRC §7216), verify every number and authority, you sign it you own it — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the five questions to ask any AI vendor. No coding required.

AI for Teachers

The practical middle ground for educators: where AI genuinely helps (lesson plans, differentiating materials into reading levels, first-draft feedback and rubrics, quiz generation, parent emails), the three non-negotiable rules — student-data privacy (FERPA), verify every fact and answer key, you are still the teacher — five workflow recipes with exact prompts, how to handle AI in students' own work (detectors are unreliable), and the questions to ask any AI vendor. No coding required.

AI for Architects

The practical middle ground for architectural practice: where AI genuinely helps (concept ideation, first-draft narratives and specs, document summaries, research starting points, early renderings), the three non-negotiable rules — you stamp it you own it, verify every code/zoning/structural claim against the adopted code, protect confidential client and site data — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

AI for Real Estate

The practical middle ground for agents and brokers: where AI genuinely helps (listing descriptions, client follow-ups, market-data summaries, process explanations, social content), the three non-negotiable rules — fair-housing compliance (AI easily generates illegal steering language), verify every fact and number, you are not giving legal/tax/appraisal advice — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

AI for Marketers

The practical middle ground for marketers: where AI genuinely helps (first-draft copy, ideation, repurposing one asset into many formats, briefs, research/analytics summaries), the three non-negotiable rules — never publish fabricated claims/stats/testimonials (FTC false-advertising risk), stay on-brand and recognizably human, verify facts and respect IP — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

AI for HR

The practical middle ground for HR and recruiting: where AI genuinely helps (job descriptions, first-draft policies, structured interview guides, internal comms, anonymized feedback summaries), the three non-negotiable rules — AI never makes a consequential decision about a person (a human decides), actively guard against discrimination/bias (EEOC + NYC Local Law 144 + EU AI Act high-risk), protect sensitive candidate/employee data — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

AI for Financial Analysts

The practical middle ground for financial analysis: where AI genuinely helps (summarizing filings and transcripts, first-draft memos and commentary, structuring/stress-testing model logic, first-pass qualitative research), the three non-negotiable rules — verify every number/calculation/source (models miscalculate and fabricate figures), MNPI and confidential data never touch an unapproved tool (information barriers, securities law), the analysis and recommendation and accountability are yours — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

AI for Journalists

The practical middle ground for reporters and editors: where AI genuinely helps (transcription, document/report summaries, headline and structure ideas, research starting points, plain-language explainers), the three non-negotiable rules — verify everything and never publish an unverified fact or quote (models fabricate quotes and sources), protect your sources and confidential material, be transparent and never pass AI text off as reporting (plagiarism/attribution) — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

AI for Product Managers

The practical middle ground for PMs: where AI genuinely helps (PRD/spec drafts, synthesizing research you actually gathered, competitive-scan starting points, pressure-testing your reasoning, stakeholder comms), the three non-negotiable rules — never let AI fabricate user data/metrics/findings (it invents plausible insights representing nobody), prioritization and product judgment stay human, protect confidential roadmap and customer data — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

AI for Sales

The practical middle ground for sales reps: where AI genuinely helps (personalized outreach from real research, follow-ups, call summaries from your notes, objection prep, CRM drafts), the three non-negotiable rules — never make a false product claim or over-promise (AI invents features/results = misrepresentation that breaks deals and trust), protect prospect data and follow call-recording/consent law, the relationship and discovery and close stay human — five workflow recipes with exact prompts, a spot-the-danger judgment exercise, and the questions to ask any AI vendor. No coding required.

The AI-Era Product Manager

How the product-manager job changes in the AI era — and how to be the PM who gets more valuable. PRDs, research synthesis and comms get cheap; judgment, taste, user truth and accountability become the moat, plus a new stack: evals literacy, the unit economics of AI features, and agent/AI UX. Five AI-augmented workflows, a judgment exercise, and a 90-day plan. The career companion to AI for Product Managers (tool literacy).

AI for Designers

A practical handbook on using AI in design: where it genuinely helps (exploring variations, first-draft copy and assets, synthesizing research you gathered) and where it must not decide (taste, brand coherence, accessibility, real user empathy). The three rules, five workflow recipes, a judgment exercise on IP and bias, and how to choose tools. No coding, no hype.

AI for Support Engineers

Support automation is the #1 deployed AI use case, so this role is changing fast. What AI automates (tier-1 tickets, FAQs, routing) versus what becomes valuable (hard troubleshooting, escalation judgment, empathy, and improving the AI itself), the new support stack, and the high-value pivots — agent-ops, AI-support engineering, and forward-deployed engineering. Five moves, a judgment exercise, and a plan.

AI for Data Analysts

Text-to-SQL and auto-dashboards are automating the mechanical half of analysis. What AI automates (writing SQL, basic dashboards, pulling numbers) versus what becomes valuable (asking the right question, judging whether data can be trusted, interpreting results in business context, and defining what the metrics mean), plus the analytics-engineering escape hatch. Five moves, a judgment exercise, and a plan.

AI for Technical Writers

AI drafts docs, generates API reference from code, and answers user questions directly — so fewer people read the docs at all. But the twist most miss: your documentation is now the context layer AI assistants, agents, and RAG systems read from, and an AI that reads wrong or unstructured docs gives confident wrong answers at scale. What AI automates (first drafts, boilerplate, formatting) versus what becomes valuable (information architecture, docs-as-context, docs-as-evals, owning the source of truth), plus the pivots — docs/DX engineering, developer relations, and AI knowledge engineering. Five moves, a judgment exercise, and a plan.

AI for Business Analysts

AI drafts requirements docs, turns meeting notes into user stories, summarizes stakeholder calls, and writes the SQL for your reports. But the hard part of business analysis was never the writing — it was asking the right questions, resolving conflicting requirements, and deciding what is actually worth building. What AI automates (documentation, first-draft stories, process diagrams, basic queries) versus what becomes valuable (problem definition, stakeholder judgment, process redesign, validating that requirements match reality), plus the pivots — analytics engineer, AI product manager, and product owner. Five moves, a judgment exercise, and a plan.

AI for Technical Program Managers

AI writes your status reports, summarizes the meeting, updates the tracker, and drafts the project plan — the reporting layer of program management is being automated. But the job was never the reports; it was driving cross-team execution, killing the risks that matter, and forcing decisions when senior people disagree. What AI automates (status, notes, trackers, first-draft plans) versus what becomes valuable (cross-org coordination, risk judgment, technical depth to challenge estimates, unblocking), how the TPM role differs from product management, and the pivots — engineering manager, product manager, and AI-program leadership. Five moves, a judgment exercise, and a plan.

AI for Scrum Masters

An honest handbook, because this role is under more pressure than most: standups, sprint analytics, backlog grooming, burndown reports, and retro summaries are exactly what AI automates — and much of the pure ceremony-facilitation scrum-master job goes with them. But coaching a team, removing organizational impediments, resolving conflict, and driving actual delivery do not automate. What AI takes over, what stays valuable, and the honest pivots — delivery lead, technical program manager, engineering manager, and org-level agile coaching. Five moves, a judgment exercise, and a plan.

AI for Developer Relations

AI drafts your blog posts, scaffolds tutorials, writes the sample code, and answers routine developer questions — and, bigger, developers increasingly ask an AI instead of reading your content at all. But that same shift creates two new high-value jobs only DevRel can do: making your product the answer the AI gives (GEO, llms.txt, docs-as-agent-context) and building the authentic community and trust no model can fake. What AI automates versus what becomes valuable, and the pivots — product marketing, product management, docs/DX engineering, and AI-visibility (GEO) specialist. Five moves, a judgment exercise, and a plan.

AI for Founders

AI collapses the cost of building — one person or a tiny team can now ship what used to take a whole engineering org. That is real leverage, but it cuts both ways: when everyone can build, building stops being the moat. What AI makes cheap (code, MVPs, first-draft everything, research) versus what becomes scarce and decisive (taste on what to build, distribution, a real wedge, customer truth, and judgment on what NOT to build), how to use AI leverage without fooling yourself, and where to place your bet. Five moves, a judgment exercise, and a plan.

AI for Game Developers

AI generates concept art, textures, 3D assets, boilerplate gameplay code, dialogue drafts, and procedural content — and it is reshaping game production faster than almost any creative field, asset pipelines first. What AI automates (asset production, boilerplate, first-draft narrative, playtest analysis) versus what becomes valuable (game design and game feel, original creative vision, knowing what is actually fun, and building AI into the game itself), plus the pivots — technical game designer, AI-gameplay engineer, and AI-leveraged indie. Five moves, a judgment exercise, and a plan.

Career Switch to Tech

Switching into tech got harder and easier at the same time, and pretending otherwise helps no one. Harder: AI now does a lot of the junior work, so the old learn-to-code-and-get-hired path is tighter and credentials count for less. Easier: AI lets a career-switcher build real, working things fast — and a portfolio of real things now beats a certificate. What changed, why your prior-career domain knowledge is an edge, how to aim above the fully-automatable entry rung, and a concrete plan. Five moves, a judgment exercise, and a realistic path.

The AI Consultant

Every company wants to adopt AI and most don't know how — the opening for AI consultants and freelancers. But building a demo is cheap; the value is positioning yourself in a specific niche, scoping projects that actually deliver, pricing on value instead of hours, and shipping real outcomes rather than prototypes that die in production. What is cheap versus valuable in AI consulting, the rules of positioning and scoping, and how to price and deliver. Five moves, a judgment exercise, and a plan.

AI for Network Engineers

AI and automation are eating the manual half of network engineering — per-device CLI config, routine changes, first-pass log analysis, and memorized command syntax. What gets automated versus what becomes valuable (network architecture and design, the hard cross-layer troubleshooting, security posture, and owning network-as-code), how AIOps changes operations, and the strong pivot into network automation and NetDevOps. Five moves, a judgment exercise, and a plan.

Blockchain to AI

A skills-transfer map for blockchain and crypto engineers moving into AI. Far more transfers than you'd guess: distributed systems, rigorous correctness, cryptography, working at scale, and incentive design are exactly what AI infrastructure needs — and skills most ML-first engineers lack. What carries over and where it applies, what you genuinely need to learn (ML fundamentals, the LLM/RAG/agent stack), how to target the systems-and-infra overlap, and a concrete plan. Five moves, a judgment exercise, and a transition path.