Feature spec
IntermediateCategory
ai-features
Build with AI
6–10 hours with Lovable or V0
Custom build
2–3 weeks custom dev
Running cost
$0–5/mo up to 100 users · $25–35/mo at 1,000 users
Works on
Everything it takes to ship an AI Based Resume Builder — parts, prompts, and real costs.
An AI resume builder needs five pieces: a multi-step input wizard, a Supabase data model for resume sections, an Edge Function calling claude-haiku-4-5 to polish bullet points, a live PDF preview via @react-pdf/renderer, and a client-side ATS keyword scorer. With Lovable or V0 you can ship a working builder in 6–10 hours for $0–$5/month at launch. Costs stay under $35/month until you reach 1,000 active users.
What an AI Resume Builder Feature Actually Is
An AI resume builder is a multi-step web form that collects a user's career history and produces a professionally formatted, downloadable PDF resume — with AI improvement available on every text section. The feature combines three concerns that are each solved separately: structured data collection (react-hook-form multi-step wizard), AI content enhancement (Anthropic claude-haiku-4-5 rewriting raw bullet points into ATS-optimised language via Supabase Edge Functions), and document rendering (@react-pdf/renderer generating PDFs in the browser without a server round-trip). The real product decisions are how many template designs to ship at launch, whether AI enhancement shows a diff for user approval or replaces text immediately, and whether users can maintain multiple resume versions for different job applications.
What users consider table stakes in 2026
- Multi-step wizard with visible progress bar — users should never see a single overwhelming form
- Real-time resume preview panel that updates within 800ms of the user stopping typing (debounced, not live per-keystroke)
- AI 'Improve this section' button on every text field, with the original text preserved and a 'Revert' option
- At least 3 distinct professional template designs selectable before export
- One-click PDF download that works consistently across Chrome, Safari, and Firefox
- ATS score indicator (0–100) with specific improvement tips — not just a number, but actionable feedback
Anatomy of the Feature
Six components — AI tools generate four of them reliably on the first prompt. The PDF preview renderer and the autosave upsert logic are where first builds reliably break.
Multi-step form wizard
UIReact component using react-hook-form + zod for per-step validation. Step state managed in React context so users can navigate back without losing data. shadcn/ui Progress bar + custom Stepper shows current position. Steps: personal info, work experience (repeating section), education, skills, certifications, summary. Autosaves to Supabase every 30 seconds via upsert.
Note: Each step must validate independently before advancing — zod schemas per-step keep error messages focused. Do not put all validation in a single schema or users see all errors at once on step 1.
Resume data model
DataSupabase `resumes` table storing each section as jsonb columns: personal_info, experiences (array), education (array), skills (array), certifications, languages. Supports multiple resume versions per user via separate rows. Row-level security restricts all reads and writes to the authenticated owner.
Note: Store experiences and education as jsonb arrays rather than normalised rows — resume sections are always read and written together, and jsonb avoids the JOIN overhead with zero schema migration risk when fields are added.
AI content enhancer
BackendSupabase Edge Function (Deno) that accepts the raw text of a single resume section plus the section type (experience_bullet, summary, skills_list). Calls Anthropic claude-haiku-4-5 with a system prompt instructing it to rewrite the content as ATS-optimised professional resume language — strong action verbs, quantified outcomes, industry keywords. Streams result back to the browser.
Note: claude-haiku-4-5 is the right model here: it is fast enough for a streaming enhancement UX and cheap enough that 10 enhancement calls per resume cost under $0.01. claude-sonnet-4-5 is overkill and 5x more expensive per token.
Resume preview renderer
UIReact component using @react-pdf/renderer that generates a live PDF preview in an iframe inside the browser — no server round-trip required. Switches between 3 template designs via a template_id prop that applies different React PDF stylesheet objects. Preview updates are debounced to 800ms after the last keystroke to avoid the expensive re-render on every character.
Note: @react-pdf/renderer runs in the browser via its web build. Do not attempt to run it in a Supabase Edge Function — Puppeteer is too heavy for Edge Function memory limits and introduces cold-start latency. Client-side rendering is the correct architecture here.
PDF export
Service@react-pdf/renderer PDFDownloadLink component renders the final PDF and triggers a browser download when clicked. Generates the same document the preview renders — same React PDF component, same data. Page size selectable: A4 (European standard) or US Letter.
Note: PDFDownloadLink is a client component — it cannot be used in a Next.js Server Component. Mark the parent as 'use client' or lazy-import it with dynamic() and ssr: false.
ATS score indicator
UIClient-side JavaScript component that counts keyword density across the resume content against a curated list of ATS-important terms per job category (technology, marketing, finance, etc.). Displays a 0–100 score with a breakdown: strong verbs count, quantified achievements count, keyword match rate. No external API — pure regex analysis runs in under 50ms.
Note: Make the score category-aware — a software engineering resume needs different keywords than a sales resume. Include the target job category as a user-selected field in step 1 so the scorer knows which keyword list to apply.
The data model
Two tables: resumes stores all resume section data as jsonb with autosave support; resume_templates is a public read-only catalogue of available designs. Run this in the Supabase SQL editor (Dashboard → SQL Editor → New query).
1create table public.resume_templates (2 id uuid primary key default gen_random_uuid(),3 name text not null,4 thumbnail_url text,5 css_class text not null,6 is_active boolean not null default true,7 created_at timestamptz not null default now()8);910create table public.resumes (11 id uuid primary key default gen_random_uuid(),12 user_id uuid references auth.users(id) on delete cascade not null,13 template_id uuid references public.resume_templates(id),14 name text not null default 'My Resume',15 personal_info jsonb not null default '{}',16 experiences jsonb not null default '[]',17 education jsonb not null default '[]',18 skills jsonb not null default '[]',19 certifications jsonb not null default '[]',20 languages jsonb not null default '[]',21 summary text,22 ats_score int,23 last_ai_enhanced_at timestamptz,24 created_at timestamptz not null default now(),25 updated_at timestamptz not null default now()26);2728alter table public.resumes enable row level security;29alter table public.resume_templates enable row level security;3031create policy "Users can view own resumes"32 on public.resumes for select33 using (auth.uid() = user_id);3435create policy "Users can insert own resumes"36 on public.resumes for insert37 with check (auth.uid() = user_id);3839create policy "Users can update own resumes"40 on public.resumes for update41 using (auth.uid() = user_id)42 with check (auth.uid() = user_id);4344create policy "Users can delete own resumes"45 on public.resumes for delete46 using (auth.uid() = user_id);4748create policy "Templates are public read"49 on public.resume_templates for select50 using (is_active = true);5152create index resumes_user_updated_idx53 on public.resumes (user_id, updated_at desc);5455create index resumes_user_created_idx56 on public.resumes (user_id, created_at desc);Heads up: The two indexes keep the 'My Resumes' list fast even when users accumulate many versions. The jsonb default values ('{}' and '[]') ensure every column is safe to read without null checks — important when autosave fires before the user completes step 1.
Build it — pick your path
Each tool fits this feature differently. Switch tabs to compare fit, time, the exact steps, and a copy-paste prompt.
Best all-round path — Lovable generates the Supabase schema, Edge Function for AI enhancement, multi-step form, and PDF preview in one project with auth already wired. The prompt below is specific enough to avoid the autosave and streaming gotchas.
Step by step
- 1Create a new Lovable project, open the Cloud tab, and confirm Supabase is connected — auth and the database will be provisioned automatically
- 2Paste the prompt below into Agent Mode and let it build the full resume wizard, preview, and AI enhancement flow
- 3Open Cloud tab → Secrets and add ANTHROPIC_API_KEY so the Edge Function can call claude-haiku-4-5
- 4Click Publish → Update; streaming AI enhancement only works on the published domain, not the preview iframe
- 5Test PDF export in Chrome and Safari — open the published URL, complete one resume, and download it on both browsers to confirm consistent rendering
Build an AI resume builder web app with Supabase backend. Schema: two tables — resume_templates (id, name, thumbnail_url, css_class, is_active) with public read policy; resumes (id, user_id, template_id, name, personal_info jsonb, experiences jsonb array, education jsonb array, skills jsonb array, certifications jsonb, languages jsonb, summary text, ats_score int, last_ai_enhanced_at, created_at, updated_at) with RLS so users only read and write their own rows. Use upsert (INSERT ... ON CONFLICT ON CONSTRAINT resumes_pkey DO UPDATE) for every autosave so no duplicate records are created. UI: Multi-step wizard with 5 steps and a shadcn/ui Progress bar. Step 1: personal info (name, email, phone, location, linkedin_url, portfolio_url, target_job_category dropdown with options: Technology, Marketing, Finance, Sales, Operations, Healthcare). Step 2: Work experience — repeating section, each entry has: company, title, start_date, end_date (or 'Present' checkbox), description textarea. Step 3: Education — repeating section: institution, degree, field, graduation_year. Step 4: Skills (comma-separated tags) and Certifications (repeating: name, issuer, year). Step 5: Review — show all entered data and trigger ATS score calculation. AI Enhancement: add an 'Improve with AI' button next to every text field and textarea. On click, call a Supabase Edge Function named enhance-resume-section. The Edge Function calls Anthropic claude-haiku-4-5 with system prompt: 'You are an expert resume writer. Rewrite the provided resume section using strong action verbs, quantified outcomes where possible, and ATS-optimised language for the target job category. Return only the improved text, no commentary.' Pass the raw text, section_type (experience_bullet | summary | skills_list), and target_job_category. Stream the result back. Store the original text in a before_ai state variable and show a 'Revert to original' button after enhancement. PDF Preview: live preview panel using @react-pdf/renderer showing the resume in the selected template design. Debounce preview updates to 800ms after last keystroke. Include a PDFDownloadLink button that generates the final PDF for download. Support 3 templates: Classic (serif, single column), Modern (sans-serif, two columns with accent colour), Minimal (clean whitespace, name as large heading). Page size: A4. ATS Score: client-side component that analyses the full resume text and returns a score 0–100 based on: percentage of experience bullets starting with action verbs (target: 80%+), number of quantified achievements containing numbers (target: 3+), keyword match rate against a 50-word list for the selected target_job_category. Display score with breakdown and 3 specific improvement tips. Autosave every 30 seconds using upsert. Handle empty sections by not rendering blank section headers in the preview. Handle AI API errors by showing a toast 'Enhancement failed — your original text is preserved' and keeping the original text visible. Support multiple resumes per user with a 'My Resumes' dashboard listing all resumes newest first.
Where this path bites
- Streaming AI enhancement does not work in the Lovable preview iframe — always test on the published URL after clicking Publish → Update
- Live PDF preview can flicker if debounce is not set to 800ms — insist on this value in the prompt if Lovable generates a shorter debounce
- @react-pdf/renderer occasionally has rendering differences between Chrome and Safari — test both browsers before launch
Third-party services you'll need
The core resume builder requires only Supabase and the Anthropic API. No additional paid services are needed for the features described on this page.
| Service | What it does | Free tier | Paid from |
|---|---|---|---|
| Anthropic claude-haiku-4-5 | AI section enhancement — rewrites raw bullet points into ATS-optimised resume language via Supabase Edge Function | No free tier — pay per token from first call | Approx $0.80/$4.00 per M input/output tokens (2026 pricing) |
| Supabase | PostgreSQL database for resume storage, auth, and Edge Function hosting for AI calls | Free tier: 2 projects, 500MB database, 50MB storage | Pro $25/mo — required for production workloads above ~50 active users |
| @react-pdf/renderer | Client-side PDF generation and live preview rendering in the browser — no server required | MIT licence — completely free | Free |
Swipe the table sideways to see pricing.
What it costs to run
Drag through the tiers to see how your monthly bill scales with users — no surprises later.
Estimated monthly running cost
Supabase free tier covers storage and auth. AI enhancement at ~3 calls per resume session and ~100 tokens per call = ~30,000 tokens/month = under $0.03 on claude-haiku-4-5. Rounding up for Supabase bandwidth gives ~$3/mo total.
Estimates use verified 2026 pricing and assume typical usage per user. Your real bill depends on activity, storage, and third-party plans.
What breaks when AI tools build this
The failures people actually hit on their first build — the symptom, why it happens, and the exact fix.
PDF preview blanks out while the user is typing
Symptom: @react-pdf/renderer is computationally expensive — re-rendering a full PDF document on every keystroke blocks the main thread and produces a blank or flickering iframe. Without debouncing, the preview updates 5–10 times per second on fast typists.
Fix: Debounce preview updates to 800ms after the last keystroke using a useDebounce hook or lodash debounce. Set the debounce value explicitly in your prompt — AI tools default to 300ms or no debounce, which is too aggressive for PDF rendering. Say '800ms debounce on preview updates' in the prompt.
Autosave creates duplicate resume records on every save
Symptom: When AI tools generate the autosave logic, they typically write a plain INSERT rather than an upsert. Every 30-second autosave creates a new row in the resumes table, so a user who spends 15 minutes building a resume ends up with 30 rows — one per autosave interval.
Fix: The Supabase client upsert requires the primary key to be included in the payload: supabaseClient.from('resumes').upsert({ id: resumeId, user_id: userId, ...data }). Generate the resume id on the client at wizard start (crypto.randomUUID()) and pass it in every upsert call. If you get duplicates, add an ON CONFLICT constraint check to your prompt.
Streaming AI enhancement is blank in Lovable preview
Symptom: Lovable's preview iframe sandboxes the app and does not support ReadableStream from Supabase Edge Functions. AI enhancement appears to work (the button is clickable, no error shown) but the streamed text never appears — the response body is silently dropped.
Fix: Always test AI enhancement on the published URL, not the preview iframe. In Lovable, click Publish → Update after wiring the Edge Function, then open the published domain in a separate browser tab. This is not a bug in your code — it is an iframe security restriction.
AI-enhanced text overwrites the user's original with no way to undo
Symptom: The default AI tool output replaces the textarea value immediately with enhanced text. If the AI enhancement is worse than the original (hallucinated job title, wrong company name), the user has no recovery path without retyping from memory.
Fix: Store the original text in a React useRef or a parallel state variable (e.g., beforeAiText) before streaming starts. Show a 'Revert to original' button as soon as enhancement completes. This is essential UX for an AI writing tool and must be explicit in your prompt.
PDFDownloadLink crashes with 'window is not defined' in Next.js
Symptom: @react-pdf/renderer accesses browser APIs at module level. When Next.js attempts server-side rendering of a page containing PDFDownloadLink, it crashes immediately with a ReferenceError — no PDF is ever rendered and the page fails to load.
Fix: Import PDFDownloadLink and the containing Resume Preview component using Next.js dynamic() with ssr: false. This defers the module load to the browser. In V0-generated code this is sometimes omitted — add a follow-up prompt: 'Wrap the PDFDownloadLink import in dynamic(() => import('./ResumePreview'), { ssr: false })'.
Best practices
Store resume section data as jsonb arrays in one row per resume — avoid normalised tables for experiences and education, since resume data is always read and written as a unit
Generate the resume id on the client at wizard start and pass it in every upsert — this is the only reliable way to prevent autosave duplicate rows
Cache AI enhancement results by content hash — if the user clicks 'Improve' on the same bullet point twice with identical text, return the cached result instead of making a second API call
Debounce PDF preview updates to 800ms — @react-pdf/renderer is too expensive to re-render on every keystroke; shorter debounces cause visible flickering on mid-range laptops
Rate-limit AI enhancement to 10 calls per user per day at launch — power users who iterate heavily generate disproportionate LLM costs before you know your usage patterns
Preserve original text in state before every AI enhancement call and show a one-click revert — users lose trust in AI tools that overwrite without an undo path
Keep the ATS scorer client-side with a curated keyword list per job category — a pure-JS implementation is faster, cheaper, and more reliable than an external API call
Test PDF export in both Chrome and Safari before launch — @react-pdf/renderer has known font rendering differences between browser engines, especially with custom fonts
When You Need Custom Development
AI tools cover the core resume builder reliably. Four scenarios push past what a prompted build can deliver:
- LinkedIn OAuth import to pre-fill the entire resume from a user's profile — requires LinkedIn Developer App approval, server-side OAuth token exchange, and profile API mapping that AI tools cannot wire without a signed LinkedIn partnership
- DOCX export with tracked changes so recruiters can mark up the document in Microsoft Word — requires docxtemplater + PizZip working from a real .docx template file, which is a fundamentally different rendering pipeline from @react-pdf/renderer
- Semantic job matching that scores the resume against a specific job description using pgvector cosine similarity — needs the full embedding pipeline (text-embedding-3-small, item_embeddings table, HNSW index) to return a meaningful gap analysis, not just keyword counting
- Multi-language resume generation with locale-specific formatting (date formats, phone number conventions, address order) and AI enhancement in the target language — requires language detection, locale-aware PDF font loading, and per-locale system prompts
RapidDev builds these features for production
Real auth, database, and payments — fixed price, delivered in weeks. You own 100% of the code.
Frequently asked questions
Can I build an AI resume builder without coding?
Yes — Lovable and V0 can generate the full feature from a detailed prompt, including the Supabase database, AI enhancement Edge Function, and PDF export. The prompt on this page is specific enough to produce a working builder in a single session. The main non-coding task is adding your Anthropic API key in the Cloud tab or Vars panel before testing AI enhancement.
How do I make resumes ATS-friendly automatically?
Two mechanisms work together: the claude-haiku-4-5 enhancement rewrites bullet points with strong action verbs and quantified outcomes that ATS systems scan for; and the client-side ATS score indicator checks keyword density against a curated list for the user's target job category. The score gives users a concrete number to improve before downloading, which drives more enhancement calls and better outcomes.
Can users have multiple resume versions for different jobs?
Yes, and it's worth building from day one. Store each resume as a separate row in the resumes table (multiple rows per user_id) and show a 'My Resumes' dashboard listing all versions. Users targeting different industries need different keyword emphasis and different template choices — this is a core feature that significantly improves retention.
What PDF library works best with Next.js for resume generation?
@react-pdf/renderer is the right choice for Next.js App Router. It renders PDF documents as React components in the browser, requires no server round-trip, and integrates cleanly with dynamic imports. The key rule: import PDFDownloadLink with dynamic() and ssr: false, because the library accesses browser APIs at module load and crashes during server-side rendering without this guard.
How do I add more template designs after launch?
Each template is a separate React PDF stylesheet object that you pass to the ResumeDocument component based on the template_id. Adding a new template means adding a new stylesheet object and a row in the resume_templates table — no database migrations, no schema changes. Design a fourth template in your stylesheet file and insert its record via the Supabase dashboard.
How do I prevent users from seeing each other's resumes?
Row-level security on the resumes table handles this entirely. The policy 'USING (auth.uid() = user_id)' ensures that every SELECT, INSERT, UPDATE, and DELETE is automatically filtered to the authenticated user's own rows — Supabase enforces this at the database level, not the application level. Even if a user constructs a direct API call with a valid anon key, they can only reach their own data.
Can I add a cover letter generator alongside the resume builder?
Yes, and it reuses the same architecture. Add a cover_letters table with the same RLS pattern, a separate Edge Function that calls claude-haiku-4-5 with a cover letter system prompt (taking job title, company, and a section of the user's resume as context), and a PDFDownloadLink component using an existing letter template. The AI enhancement infrastructure is identical — a cover letter generator adds roughly 2–3 hours on top of a working resume builder.
Can the AI import data from LinkedIn to pre-fill the resume?
Not via a simple prompt — LinkedIn's profile API requires OAuth 2.0 authentication and a LinkedIn Developer App with specific permissions (r_liteprofile, r_emailaddress). AI tools cannot wire OAuth provider integrations automatically. This is a custom development task, typically adding 3–5 days to the build, and requires LinkedIn Partner Programme access for full profile data at scale.
Need this feature production-ready?
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