How to Build Apps with AI: From Idea to API (Or X)

Can AI help you build useful apps without settling for slop? This guide shows a human-led workflow for taking a product idea through requirements, feature-level implementation, API design, and verification. I use Orbit, a Slack-style team chat demo, as a case study. You can apply the process to your own product, whether you write code or are still shaping the idea. My first prompt was simple: “I want to build a Slack clone.” That sentence named a product category. It did not say who the product was for, what the first version had to do, what “done” meant, or which parts could be simulated. On social media, I often see two absolute claims: AI only builds “slop,” and AI cannot help create applications that reach production. Both claims miss the role of the people building the product. AI can help create useful software when people set the scope, review the changes, test the behavior, and own the release decision. AI-generated code cannot certify itself as production-ready. Orbit is a demo, not a production application. It has no sign-in, uses one shared public demo owner, and loses stored data when its process restarts. I will show what the AI-assisted workflow produced and what still needs verification before a production release. The prompt examples are teaching examples, not transcripts of the original conversations. In this guide - Why AI output can be useful - Orbit, the running example - Turn the idea into requirements - Build and review features - Derive an API from accepted behavior - What production readiness requires - Apply the workflow to your product - Further reading Why AI output can be useful “Slop” is a fair description of output that looks complete but does not meet the user’s needs. A vague request gives an AI room to guess. If no one checks those guesses against real requirements, the result can look polished while behaving like the wrong product. The process for Orbit added checks at each handoff. ChatGPT helped expose missing decisions. Claude organized the decisions in a PRD and feature specs. Grok Build implemented bounded changes. I reviewed the UI, revised the requirements, and asked for an API audit only after the UI behavior was accepted. Feature acceptance criteria and tests made some results observable. That work is evidence that AI can contribute to product planning, interface work, API design, and implementation. It does not prove that Orbit is production-ready. The current demo has no sign-in, shared demo data, and process-scoped persistence. The last implementation notes report focused API and compose tests passing, but also note broader baseline failures and a blocked browser journey. The sections below describe both the work and its limits. Orbit, the running example The screenshots follow a new visitor from the welcome screen to a channel, a thread, and a direct message. The welcome screen tells visitors that they share demo data. There is no sign-in. Do not enter private messages. Calls are simulated; they do not carry live audio or video. The welcome screen explains the shared demo before a visitor enters. The seeded workspace lets a reviewer see a populated channel at once. The channel shows the navigation, conversation, and composer in one view. The thread pane keeps replies beside the message that started them. A thread is a separate conversation path inside the channel. Direct messages use the same basic layout with a different recipient and history. A direct message has its own conversation history. These screens helped me judge the product's direction. They did not prove that every control behaved as required. I used the feature PRDs and acceptance criteria to review behavior. Turn the idea into requirements before asking for code “Build a Slack clone” leaves many decisions open. Does the first version need accounts, real-time messages, calls, file uploads, mobile navigation, or search across every message? If a model answers those questions by guessing, it can build a polished interface for the wrong product. I used ChatGPT to make the missing decisions visible before I asked Claude to write the PRD. For your own product, replace the chat-specific questions with the users, tasks, data, and risks that matter in your domain. Keep the same method: ask the AI to expose unknowns before it writes a specification. Prompt 1: clarify the idea I want to build a Slack-style team chat app. Help me clarify the idea before you write a build prompt. Ask questions about: - who will use the app and what they need to do; - the smallest useful first version; - workspaces, channels, direct messages, threads, and search; - accounts, data ownership, persistence, and real-time behavior; - files, calls, mobile use, and accessibility; - security, edge cases, and features that are out of scope. Separate confirmed facts, assumptions, recommendations, and open questions. Do not turn an unanswered question into a requirement. Do not write code. When the important questions are answered, write a structured brief I can give to another assistant to draft a PRD. The key instruction is to separate what I decided from what the model suggested. If the answer says “accounts are required” but I never chose an account system, that is an assumption to review, not a requirement to pass along. For a non-technical builder, describe the user and the problem in everyday language. For a technical builder, add the stack, integrations, data boundaries, and deployment constraints. Either way, ask the model to list unanswered questions instead of filling them in. What BRD, PRD, and SRS mean The document names vary between companies. Teams also combine them. These are common meanings: - A BRD, or Business Requirements Document, describes the business problem, stakeholders, expected outcomes, constraints, and success measures. - A PRD, or Product Requirements Document, describes users, product outcomes, workflows, feature priorities, and acceptance criteria. - An SRS, or Software Requirements Specification, describes testable software requirements, interfaces, constraints, and verification details. The terms are related, but they do not mean the same thing. An SRS is a software requirements specification, not a “software specification requirement.” Formal standards use their own artifact names. ISO/IEC/IEEE 29148 describes requirements engineering across the system and software lifecycle, including business and software requirements specifications. For Orbit, Claude drafted the product requirements document, and we later created eight feature PRDs. We did not create separate BRD or SRS documents for this project. The feature PRDs and API contract captured the detail we used to build and review the app. Prompt 2: ask Claude for the PRD Use the brief below to draft a Product Requirements Document for Orbit. Include: 1. product problem, users, goals, and success measures; 2. priority user journeys; 3. functional requirements with testable acceptance criteria; 4. non-functional requirements for security, privacy, accessibility, responsive behavior, and performance; 5. data and integration needs; 6. non-goals, dependencies, risks, and open questions. For every requirement, mark its source as confirmed, assumed, or proposed. Do not invent business decisions. Put unresolved choices in an open-questions section. Use plain language. Do not write code. A PRD gives the people building the product one description of the user, the goal, and the expected behavior. It also gives the AI a boundary. “Build Slack” is not a boundary. Build the first UI, then break the work into features Once Claude drafted the PRD, I passed it to Grok Build. The first design looked close to what I had in mind. That was a useful checkpoint. It was not a reason to assume that every behavior had been defined. Prompt 3: give Grok Build the approved scope Read the attached Orbit PRD and the existing project instructions. First summarize the user, the first-release scope, and the acceptance criteria. List conflicts and unanswered questions before changing the app. Build the interface and behavior described in the approved scope. Keep simulated behavior visibly separate from real persistence or services. Do not add features because they are common in Slack. After the change, report what works, what is simulated, what is incomplete, and which acceptance criteria you could not verify. The line about features common in Slack prevents scope creep. A clone brief does not automatically include every feature in the original product. When the first interface looked right, I returned to Claude and asked: “For this application, list out the features.” I turned that list into separate feature PRDs. Then I fed those documents to Grok Build one at a time. Prompt 4: ask for feature-level PRDs Break the approved Orbit PRD into feature-level PRDs. For each feature, include: - user problem and user story; - in-scope behavior and explicit non-goals; - normal flow, edge cases, and empty/error states; - data and permission rules; - dependencies on other features; - measurable acceptance criteria. Keep accessibility and responsive requirements explicit. Link each feature to the parent PRD requirement. Flag overlap or conflict. Do not silently resolve it. Separate the first release from later work. Each feature needs a clear boundary. For example, “make channel switching smooth” is hard to verify. “When a user selects a channel, update the header, messages, composer placeholder, draft, and active sidebar item together” describes a visible result. Prompt 5: implement one feature at a time Implement only the attached feature PRD: [feature name]. Before editing, summarize its acceptance criteria and identify the relevant existing UI and tests. Ask about any conflict with the parent PRD instead of guessing. Keep unrelated features unchanged. After editing, report each acceptance criterion as verified, not verified, or blocked. Show the behavior change and remaining gaps. Do not claim