Social Apps Checklist for Vibe Coding

Describe the core loop in plain language, such as post, react, reply, follow, or join group, before asking an AI builder to scaffold the app. This reduces vague prompts that produce disconnected features and helps conversational coding tools generate matching database models and UI states.

Pick a single behavior like discussion threads, creator updates, private circles, or event-based networking. Social apps built with AI often overgenerate features, so narrowing the first behavior keeps prompts focused and avoids shipping a confusing hybrid product.

List guest, member, moderator, admin, and creator permissions in a table before asking the AI to build auth and access rules. This is especially important when using tools that scaffold full-stack apps quickly, because vague role logic leads to broken moderation and privacy controls.

Specify which objects are visible to everyone, followers, approved members, or only the author. AI-generated social platforms often default to open visibility, which can create privacy leaks if your prompts do not explicitly cover content scopes.

Include scenarios like blocked users, deleted posts, edited comments, pending invites, and account suspension. These examples give AI coding assistants the context needed to generate more realistic workflows instead of only happy-path CRUD screens.

A profile network, a feed app, and a group-based community need different schemas and navigation patterns. Clarifying this upfront helps you prompt for the right homepage, discovery logic, and data relationships instead of rebuilding the app architecture later.

Prompt for users, profiles, posts, comments, reactions, follows, memberships, reports, and notifications as data objects with relationships. This avoids the common vibe coding mistake of building attractive pages first and discovering later that the backend cannot support core social actions.

Posts, comments, messages, and accounts should usually be restorable or reviewable rather than permanently deleted. Soft delete flags support moderation, appeals, and auditability, which matter much more in social apps than in simple SaaS forms.

Tell your AI tool to add per-user and per-IP limits for key actions, especially if you are using a backend like Supabase, Firebase, or a serverless API. Social products attract spam quickly, and AI-generated backends rarely add abuse controls unless directly prompted.

Prompt for indexes on created_at, user_id, group_id, visibility, and engagement counters to support timelines and discovery pages. Many vibe-coded apps feel fine with ten users but collapse when feed queries turn into full-table scans.

Store likes, comment counts, follower counts, and repost totals in a way that can be recalculated or updated asynchronously. AI-generated schemas often write counters directly into core tables without considering race conditions or eventual consistency.

Ask for logs covering report creation, content takedowns, role changes, login anomalies, and failed invite attempts. This creates a foundation for later review tools, support operations, and trust metrics that social platforms need as communities grow.

A new user with no connections, groups, or content needs guided next steps like follow suggestions, starter prompts, or community onboarding. AI-generated interfaces often leave social products feeling dead because they do not explain how activity starts.

Tell the AI to support character limits, media validation, link previews, and save-as-draft behavior if relevant. Social posting is more nuanced than a basic form, and weak generation here leads to broken content quality and user frustration.

Profiles, usernames, avatars, badges, role labels, and timestamps should appear consistently across feeds and threads. This helps trust and context, especially in communities where anonymous or pseudonymous participation can create confusion if presentation is inconsistent.

Deep replies, nested discussions, and quote responses become messy fast without explicit interface rules. Ask the AI to limit nesting, collapse long branches, and preserve readability across mobile layouts to avoid unusable conversation views.

Do not leave preferences until later, because social friction often comes from unwanted alerts or unwanted interactions. Prompting these controls early forces the underlying backend to support notification rules, mute lists, and user blocks correctly.

Features like circles, pinned posts, admin tools, and advanced filters should appear when relevant rather than cluttering the first session. This is a practical way to keep AI-built interfaces usable when your prompt set includes many community features.

Ask the AI to generate structured report reasons, optional notes, and an admin review queue rather than a generic contact form. Social apps need object-level reporting so moderators can act quickly and consistently.

Blocking should remove direct interaction, muting should reduce visibility, and restricting can limit harassment without creating escalation. AI tools often merge these concepts into one blunt feature unless you specify each workflow.

Content should move through states such as active, flagged, hidden, removed, and appealed. This gives moderators operational clarity and prevents the common vibe coding problem of deleting content instantly with no review history.

Use keyword filters, link domain checks, velocity detection, and duplicate post detection as first-line defenses. Even a small community can be overwhelmed if your AI-built social app launches with no automated trust layer.

Require email verification, CAPTCHA, or step-up verification before posting links, sending many invites, or creating multiple groups. This reduces bot abuse and fake engagement, both of which can poison early community quality.

Your AI prompt should request filters by report type, status, date, user, and content object so moderators can triage efficiently. A social product without searchable moderation tools quickly turns into manual database work.

Generate test users, varied posting patterns, reply chains, follows, reports, and dormant accounts to see whether the app still feels coherent. Vibe-coded social products often look polished until realistic network activity exposes bad feed logic and weak layouts.

Try spam bursts, self-replies, mass follows, ban evasion, offensive media uploads, and deleted-account edge cases. This is where AI-generated apps frequently fail, because initial prompts tend to optimize for feature demos rather than adversarial behavior.

Track metrics like first profile completion, first post, first reply received, first follow, first group join, and seven-day return. These signals show whether your AI-built social app is creating interaction loops instead of just traffic.

Prompt the AI to move expensive actions such as push notifications, email digests, media processing, and feed fan-out into background jobs. This keeps the user experience responsive and helps the app survive growth beyond the prototype phase.

Even if the app was built conversationally, inspect route guards, row-level security policies, and client-side assumptions before launch. Social apps expose personal data and relationship graphs, so sloppy generated auth code is a serious risk.

Document which prompts generated which modules, what manual fixes were applied, and where AI output still feels fragile. This is especially valuable for non-technical founders who need to maintain or sell a social app later without losing context.