August 25, 2025 • Written by AiResumeGen Editor
Hey there, fellow developers and job seekers! If you've ever stared at a job description and wondered how to tweak your resume to make it scream "perfect fit," you're not alone. I've been there—endless revisions, keyword stuffing, and that nagging doubt if it's enough. That's why I built the job tailoring feature for AIResumeGen.com, an AI-powered tool that automatically adapts your resume to a specific job spec. In this post, I'll walk you through the entire process of building it, from the initial idea to the nitty-gritty implementation. Think of it as a behind-the-scenes tour of turning a basic resume parser into a job-matching powerhouse.
I'm a full-stack developer with a passion for AI-driven tools that solve real-world problems. This feature started as an enhancement to the existing resume parsing app at AIResumeGen.com, but it quickly evolved into something much more powerful. Let's dive in.
Resumes are tricky. They're your ticket to an interview, but one size doesn't fit all. In the current job market (as of 2025, with AI screening resumes left and right), customization is key. The original app could parse a resume from PDF or text, extract structured data like skills and experience, and even save it for authenticated users. It worked fine, but it was static—no adaptation to the job at hand.
The goal? Make it dynamic. Users could upload a job spec (PDF, text, or pasted snippet), pick a tone (Formal, Neutral, or Creative), add extra context, and boom—get a tailored resume that highlights relevant skills, aligns with the company's values, and matches the required experience. No more manual edits; let AI do the heavy lifting while keeping the human touch.
Key requirements:
This wasn't just about adding features; it was about creating a seamless flow that feels magical to the user.
Before building new stuff, I audited what I had. The app was built with Next.js, using Gemini for AI parsing and Zod for schema validation—solid choices for reliability.
High-level flow:
/api/parse-resume) validates, parses (AI for PDF/text, regex fallback), merges data, saves if authenticated, and returns structured JSON.Key components included:
lib/resume-parser/ai-parser.ts.regex-parser.ts.ResumeDatabase in lib/database.ts.It was modular, which made extending it easier. No need to rip everything apart—just plug in enhancements.
I love starting with a high-level design. I sketched a flowchart to visualize the new "enhanced" flow:
flowchart TD
A[Client Form\nresume + jobSpec + tone + context] --> B[/api/parse-resume-enhanced/]
B --> C{Job Spec?}
C -- No --> D[Legacy Parser\n(existing flow)]
C -- Yes --> E[Job Spec Extraction\n(jobSpecExtractor)]
E --> F[Prompt Factory\n(dynamicPromptGenerator)]
D & F --> G[AI Rewrite Service\ntailorResume()]
G --> H[Persistence Layer\nResumeDatabase (+Context)]
H --> I[Response to Client\n{ tailoredResume, meta }]
Green nodes were the new ones: extraction, prompt generation, and tailoring. This kept the legacy path intact for users who didn't need tailoring—backward compatibility is king!
Breaking it down:
/api/parse-resume to /api/parse-resume-enhanced to handle new form fields like jobSpecFile, jobSpecText, tone, and extraPrompt.positionTitle, requiredSkills array, yearsExperience).UserAdditionalContext interface for storing job spec source, text/file URL, tone, and extra prompt. Database got a new JSONB column for this.For the Zod schema on job specs:
import { z } from 'zod';
export const jobSpecSchema = z.object({
positionTitle: z.string().describe('Role title, e.g., "Frontend Engineer"'),
requiredSkills: z.array(z.string()).describe('Stack & soft skills keywords'),
yearsExperience: z.number().int().min(0).optional().describe('Minimum total years of experience'),
responsibilities: z.array(z.string()).optional(),
companyValues: z.array(z.string()).optional(),
});
Prompt engineering was crucial. The system message positions the AI as an "expert resume writer," with instructions to align wording, emphasize matches, maintain chronology, and apply tone—all while respecting length limits. To illustrate the prompt building process, here's a simple sequence diagram:
sequenceDiagram
participant User as User Input
participant Extractor as JobSpecExtractor
participant Generator as DynamicPromptGenerator
participant LLM as AI (Gemini)
participant DB as Database
User->>Extractor: Submit Job Spec
Extractor->>Generator: Structured Job Data
User->>Generator: Resume, Tone, Extra Context
Generator->>LLM: Dynamic Prompt
LLM->>DB: Tailored Resume
DB->>User: Saved & Returned Resume
With design in hand, I rolled up my sleeves. Here's the phased approach I took:
Database Migration: Added a additional_context JSONB column to the resumes table. This stores the tailoring metadata without bloating rows. I used a migration tool to keep it reversible.
Zod Schemas First: Created jobSpecSchema and userAdditionalContextSchema. Schemas act as contracts—validate early, fail fast.
Job Spec Extractor: In jobSpecExtractor.ts, I call Gemini's generateObject() with a prompt to extract data. Fallback to regex for dev mode or offline testing. Example: If the job spec mentions "5+ years in React," it pulls yearsExperience: 5 and requiredSkills: ["React"].
Prompt Generator and Tailorer: dynamicPromptGenerator.ts builds a string like: "Rewrite this resume [JSON] to match this job [JSON] in a [tone] style. Extra: [prompt]." Then tailorResume.ts feeds it to the LLM and validates the output.
API Refactor: The new route checks for job spec presence. If yes, branch to extraction and tailoring; else, fallback to legacy. Parallelized extraction and parsing for speed.
Persistence Updates: Extended ResumeDatabase.saveResume() to include the new context. If authenticated, it saves everything.
Testing and Polish: Wrote unit tests for parsing accuracy (using fixtures) and snapshot tests for prompts. Load-tested LLM calls and added caching for duplicate job specs (hash the content). Feature flag IS_JOB_TAILORING_ENABLED for safe rollout.
Throughout, I reused existing utils like createSlug and config flags to avoid duplication.
No build is smooth sailing. Here's what tripped me up and how I fixed it:
LLM Latency and Cost: Tailoring takes time. Mitigation: Implemented streaming responses so the client gets partial results progressively. Also, cached extracted job specs to avoid redundant API calls.
Inaccurate Extractions: AI isn't perfect. Solution: Zod validation with confidence scores—if low, fallback to regex or prompt the user for manual input.
Schema Drift: As I iterated, types could misalign. Fix: Leaned on TypeScript's z.infer<> for strong typing and added integration tests.
Performance Hits: Large JSONs in DB. I considered compressing or splitting tables but started simple with JSONB limits in mind.
One big lesson: Prompt engineering is an art. I iterated dozens of times, testing with real resumes and job postings to ensure the output felt natural, not robotic.
After weeks of coding, testing, and tweaking, the job tailoring feature went live on AIResumeGen.com. Now, users can upload their resume and a job ad, pick "Creative" for that quirky startup role, add "Emphasize my open-source contributions," and get a tailored version in seconds. It's saved me (and hopefully others) hours of manual work.
This project reinforced modular design's power—new features slotted in without breaking the old. If you're building similar tools, start with schemas, embrace fallbacks, and test prompts obsessively.
What's next? Maybe integrating more LLMs or adding cover letter generation. If you try AIResumeGen.com or have feedback, hit me up in the comments. Happy job hunting, and remember: Your resume is your story—make it fit the chapter.
Thanks for reading! If this resonated, clap, share, or follow for more dev journeys. 🚀