Ship Your First AI Product (Founder Track)

Founder speed is wasted on the wrong problem. Start by validating a painful, repeated user job and proving AI is the right tool before you write your first prompt.

1. 1Zero to Onemanual12 min readThis is the founder filter for what to build now, what to ignore, and what to defer until users are pulling the product out of your hands.
2. 2Product-Market Fitmanual11 min readRead this before you build a single AI workflow so you do not ship a clever assistant into a market that does not care.
3. 3Customer Interviewsmanual12 min readTen blunt interviews will save you months of vibe-based building and tell you where users will actually pay for speed.
4. 4When AI Is the Right Answer (and When It Isn't)manual13 min readUse this screen to kill generic AI ideas early and keep only problems where probabilistic output is an advantage, not a liability.
5. 5Notion AI — Adding Intelligence Without Breaking Trustcase7 min readNotion shows what it looks like to add AI only where users already trust the workflow, instead of spraying AI across every surface.

Model choice is a business decision, not a taste decision. The winning move is usually the cheapest model that clears quality for your use case, not the frontier default.

1. 1The Model-Selection Laddermanual12 min readRun the ladder exactly as written and promote models only when eval evidence says you must, not because launch week feels high stakes.
2. 2Cost & Latency as First-Class Product Constraintsmanual13 min readThis chapter gives you the math for cost-per-user and response-time budgets before inference bills become your surprise board update.
3. 3The 2026 Model Landscapemanual13 min readUse the landscape as a map, then ignore hype and buy the rung that meets your bar at the lowest recurring cost.
4. 4Cursor — The AI Code Editor That Competed with GitHubcase6 min readCursor is the playbook for wrapping strong models in product UX that users pay for, without pretending model ownership is required.
5. 5GitHub Copilot — The First Real AI Product, and What Five Years Taught Uscase14 min readCopilot is the long game proof that latency, acceptance rate, and workflow fit beat raw demo magic over time.

Prompting is product specification in disguise. If you cannot define good outputs and run a small regression eval, you are shipping faith, not product quality.

1. 1Prompt Design as Product Designmanual12 min readTreat your prompt like a PRD with constraints, edge cases, and tone rules that survive handoffs across tools and teammates.
2. 2Eval Before Launchmanual15 min readBuild a small sharp golden set now so every prompt or retrieval change is tested before users find regressions for you.
3. 3Harvey — Vertical AI for a High-Stakes Professioncase8 min readHarvey shows how vertical AI wins by narrowing scope, instrumenting quality, and making trust auditable instead of implied.
4. 4Klarna — When AI Customer Support Was Half-Rightcase17 min readKlarna is the reminder to separate strong narrow-scope outcomes from company-wide claims that collapse under scrutiny.

Language model errors are a product reality, not an exception path. Your UX needs recovery, confidence cues, and clear boundaries before launch day.

1. 1Hallucination as a Product Problemmanual15 min readThis chapter reframes hallucination from model bug to product design responsibility, which is where founders can actually control risk.
2. 2AI UX Patterns That Workmanual13 min readCopy the patterns that preserve trust under uncertainty and skip the chat theater that looks smart but fails in real tasks.
3. 3Perplexity — Search Rewritten as Conversationcase6 min readPerplexity is the best case study in citation-first answer UX when your product is replacing links with generated claims.
4. 4Air Canada — The Chatbot That Made a Promise the Airline Had to Keepcase15 min readAir Canada is what happens when the UI cannot recover from wrong model output and the business pays the legal bill.

Speed comes from choosing the smallest architecture that works this week. Build with tool use and retrieval only when the simpler rung fails your eval bar.

1. 1Tool Use, Function Calling, Agents — The Maturity Laddermanual12 min readUse the agent ladder to avoid overbuilding orchestration when one scoped tool call would have shipped faster and safer.
2. 2RAG, Fine-Tune, or Context Window?manual16 min readLearn when retrieval-augmented generation (RAG) earns its complexity, and when context-window prompts or fine-tuning are the cleaner move.
3. 3Anthropic — Research Lab to Product Companycase7 min readClaude's product evolution is the clearest look at balancing capability, safety, and iteration speed in a live assistant product.
4. 4paths/ai-build-alongs-2026-q2↗externalBuild-along recordings for Claude Code, Cursor, and Replit are landing in this companion path and are worth using as your weekly shipping cadence.optional

The path ends when one real user completes one real workflow in your product. Your deliverable is a short ship doc with the feature spec, eval set, failure-handling UX notes, and a defendable cost-per-user number from actual usage.

1. 1A/B Testing & Experimentationmanual9 min readUse this to design the fastest credible live test so your first launch teaches you something decision-grade, not anecdotal.
2. 2Measuring Outcomesmanual10 min readDefine the one behavior metric and one quality metric that will decide whether to iterate, expand, or kill the feature.
3. 3Linear — AI as a Quiet Utility, Not a Chat Assistantcase14 min readLinear is the benchmark for quiet, useful AI that earns repeat usage by reducing friction instead of demanding attention.