Workflow
AI Product Requirements Workflow
Turn a fuzzy business need into requirements a team can build from — interrogate the need into concrete requirements, shape them as user stories, and write the PRD.
Build an Applicant Tracking System with AI
The full path to an applicant tracking system — model jobs, candidates, and hiring stages, generate job descriptions and screening prompts, parse résumés into structured data, design the hiring API, set roles, review security, then ship.
Overview
An applicant tracking system is a hiring pipeline, not a sales CRM and not generic work tracking. Its objects are jobs, candidates, résumés, interview stages, and hiring decisions — and the work that makes it AI-powered is specific: generating consistent job descriptions and screening prompts, and parsing messy résumés into structured candidate data you can compare. This project builds that system end to end: it frames the requirements, models the jobs/candidates/résumés/stages, generates the job-description and screening prompts, extracts structured data from résumés, designs the hiring API and its workflow actions, sets the roles and permissions, reviews the security and sensitive-candidate-data handling, and prepares the release. It is deliberately not a CRM (sales contacts and deals) and not a project-management tool (generic tasks) — it's a focused hiring system. NewPrompt designs the system and generates the prompts, schemas, and contracts for each stage; you run those in your own AI tool and own the data and the hiring decisions. It does not screen or hire candidates for you, store résumés, or run AI on its servers — it gives the build a hiring pipeline that's structured and reviewed for the sensitive data it handles.
The journey
Each stage runs a NewPrompt workflow, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
See the execution map →Define & Scope
Clarify what you're building and for whom.
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Define the hiring system requirements
Frame what the ATS must do — the roles it serves, the hiring stages it tracks, and what 'done' means for a candidate — before modeling anything, so the system fits the hiring process instead of forcing it.
Outcome Requirements for the jobs, candidates, and hiring stages the system tracks.
Design
Design the right solution before building.
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Model jobs, candidates, and pipeline stages
Design the data the ATS runs on — jobs, candidates, résumés, applications, and the stages a candidate moves through — as a schema that reflects the hiring process, not a generic contacts table.
Outcome A jobs/candidates/applications/stages schema fit to the hiring process. -
Design the hiring API and actions
Design the API the ATS is operated through — create a job, advance a candidate, record an interview, make a decision — as a contract, so the UI and integrations develop against a stable surface.
Outcome An API contract for the hiring workflow's actions. -
Define roles and access control
Model who can see and do what — recruiters, hiring managers, interviewers — so sensitive candidate data and hiring decisions are scoped to the right people, not visible to everyone.
Outcome Roles and permissions for hiring data and decisions.
Build & Refine
Build, test, secure, and make it production-ready.
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Generate job descriptions and screening prompts
Build the hiring-specific prompts — consistent job descriptions and the screening criteria each role is judged against — so postings and screening are structured and repeatable instead of rewritten each time.
Outcome Reusable job-description and candidate-screening prompts. -
Parse résumés into structured data
Extract the structured candidate data from résumés — skills, experience, education, contact — handling the messy formats, so candidates can be compared on real fields instead of read one by one.
Outcome Structured candidate data extracted from résumés. -
Review security and candidate-data handling
Review the system the way an attacker would — access to candidate records, the API, the integrations — and back the findings with tests, because an ATS holds sensitive personal data that must not leak.
Outcome Access control and candidate-data handling reviewed for vulnerabilities.
Ship & Validate
Ship with confidence and validate results.
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Prepare the release
Cross from built to live — readiness checks, a deploy and rollback plan, and the signals to watch — so the ATS launches deliberately and a problem is caught before it affects a live hiring round.
Outcome The ATS shipped with a rollback path and launch checks.
Expected outcome
An applicant tracking system designed end to end — a jobs/candidates/stages data model, generated job descriptions and screening prompts, structured résumé parsing, a hiring API, roles and permissions, a security review of candidate-data handling, and a release plan — so hiring runs on a structured pipeline instead of a spreadsheet and an inbox.
Best for
- Managing jobs, candidates, résumés, and hiring stages in one system
- Generating job descriptions and parsing résumés into structured data
- Teams that need candidate-data handling reviewed for sensitivity
Not for
- Tracking sales contacts and deals — that's the CRM project
- Generic task or work tracking — out of scope by design
- Automated hiring decisions or candidate scoring that replaces human judgment — this path supports tracking and screening assistance, and you own every hiring decision
FAQ
How is this different from the CRM project?
A CRM tracks sales contacts, deals, and pipeline. This tracks a hiring pipeline — jobs, candidates, résumés, interview stages, and hiring decisions — with résumé parsing and job-description generation as the AI core. Different objects, different journey.
Does NewPrompt screen or hire candidates?
No. NewPrompt designs the system and generates the prompts, schemas, and contracts. You run them in your own AI tool, supply and store the candidate data yourself, and own every hiring decision. NewPrompt does not store résumés or run AI.
Why is there a dedicated security stage?
An ATS handles sensitive personal data — names, contact details, résumés. The journey includes a security review of access control and candidate-data handling specifically because that data carries real privacy and compliance weight.
How do I build an applicant tracking system with AI?
Start by framing the hiring requirements, then model jobs, candidates, résumés, and stages; generate job-description and screening prompts; parse résumés into structured data; design the hiring API; set roles; review security; and prepare the release. AI structures each stage's prompts and schemas; you run them and own every hiring decision.
What hiring process do I need before building an ATS with AI?
Define your real hiring pipeline first — the roles you hire for, the stages a candidate moves through, and what 'done' means for a candidate. The first journey stage frames these requirements so the system fits your process instead of forcing a generic one. Without that, the data model and screening criteria have nothing accurate to reflect.
How do I reduce bias in an AI-assisted ATS?
Bias is addressed with transparent, job-related screening criteria and human oversight — not eliminated or guaranteed away. This blueprint has you write consistent screening prompts tied to each role and keep human reviewers owning every candidate decision. AI helps structure and apply criteria evenly; it does not rank or decide who to hire.
Workflows in this project
Workflow
AI Database Design Workflow
Design a schema on its data, not a hunch — model the entities and relationships, set the constraints that protect integrity, plan indexes around real queries, then document the schema and migration.
Workflow
AI Hiring Workflow
Run hiring the same way for every role — build a reusable job-description template, lay out a consistent screening sequence, and extract structured data from resumes instead of eyeballing each one.
Workflow
AI Data Extraction Workflow
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Workflow
AI API Design Workflow
Design an API on its contract instead of discovering it endpoint by endpoint — model the resources, design the endpoints and payloads, pin the contract, then review it before code locks it in.
Workflow
AI Auth & Identity Workflow
Design access control before you build it, not after a breach — choose the authentication approach, model the roles and permissions, review the design for gaps, then document the identity model.
Workflow
AI Security Review Workflow
Review code for what an attacker would do, not just what tests catch — anchor the model as a security engineer, run a threat-focused review, then back the findings with auth and input tests.
Workflow
AI Deployment & Release Workflow
Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.
Resources used in this project
Resource
Product Requirements Assistant
Turn a rough feature idea into a structured requirement: problem statement, acceptance criteria, and what's explicitly out of scope.
Resource
Schema Design & ERD Prompt — Entities, Relationships, and Keys
Turn product requirements and key entities into an ERD-ready schema spec: tables, fields, primary and foreign keys, relationships with cardinality, indexes, and constraints — with the modeling gaps named as open questions.
Resource
Job Description Variable Builder
Documents variables in job description prompt templates — role title, seniority, responsibilities, required skills, benefits, location, and hiring process.
Resource
Extract Resume Information with AI
Candidate name, current role, years, skills, education — résumés into consistent screening records, with inference kept on a short leash.
Resource
Turn a JSON Schema into a Prompt
You have the schema — fields, types, requirements. The translation into a prompt the model actually follows: schema lines, realistic example, and validation rules.
Resource
RBAC Design Prompt
Design a role model that scales — roles derived from real permissions, the role-permission mapping, and the inheritance and edge cases before you hardcode 'isAdmin'.
Resource
Security Code Review Prompt
Twelve security checks — injection, auth, secrets, SSRF, privilege escalation — reviewed the way an attacker would read the code.
Resource
Production Readiness Review Prompt
SHIP or DO NOT SHIP: twelve checks on failure modes, observability, rollback, and load — the review that happens before the incident.
Tools used in this project
Tool
System Prompt Generator
Build structured system prompts from role, tone, constraints, and model target.
Tool
SQL Optimization Prompt
Build evidence-based SQL optimization prompts — goal, platform, and the evidence you have turn into a query tuning contract.
Tool
Prompt Variable Builder
Turn any plain prompt into a reusable, variable-based template — auto-detect values and export.
Tool
Extraction Prompt Generator
Build prompts that extract defined fields from unstructured text — emails, invoices, tickets, résumés.
Tool
JSON Output Prompt Builder
Build prompts that return structured data — JSON first, with YAML, XML, and CSV modes — parseable every time.
Tool
Multi-Step Prompt Builder
Break a big goal into a sequential prompt workflow — each step with an objective, prompt, and expected output.
Tool
Code Review Prompt Generator
Generate code review prompts for any scope — snippet, file, diff, or full pull request — with focused review rules.
Tool
Markdown Output Builder
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
Ways to Use This Project Path
Practical project ideas you can build from this base project path — each opens in the Project Advisor.
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