How to build an AI resume assistant using RAG and vector matching

Millions of job seekers paste their resumes into general-purpose LLMs with the prompt: "Rewrite my resume for this senior developer job." The result is often disastrous.

Because standard models lack grounding constraints, they invent technologies the applicant never touched, inflate metrics with imaginary percentages ("boosted revenue by 400%"), and emit generic corporate buzzwords that trigger automated rejection from hiring managers.

When an applicant is called into a technical interview and asked to explain the distributed consensus algorithm that the AI hallucinated onto their resume, the fraud is instantly exposed.

A RAG-based resume assistant solves this crisis by enforcing factual containment. It indexes the candidate’s complete, verified career history — every project, technology, promotion, and metric — and strictly forbids the generation of any bullet point that cannot be attributed to a real source chunk.

Instead of limiting the applicant to a single one-page PDF, a RAG resume assistant maintains a Master Career Knowledge Base. This includes past performance reviews, GitHub project READMEs, technical design documents, and exhaustive bullet points from every job the user has held over the past decade.

• Document Ingestion & Sectioning: The master career history is parsed into distinct role blocks and accomplishment chunks (e.g., "Led Kafka Migration at Stripe: 2023").
• Dual Embedding & Tagging: Each accomplishment is embedded into a vector store (like pgvector) and tagged with explicit structured metadata: company, years of tenure, primary programming languages, and verified metrics.
• Target Job Description Parsing: When the user enters a job link, the system extracts the target job requirements: must-have skills, preferred credentials, and domain responsibilities.
• Semantic Gap Analysis: The system runs vector similarity between each job requirement and the candidate’s master chunks, highlighting requirements with zero matching evidence.

The most valuable feature of a RAG resume assistant is its ability to surface forgotten achievements that directly match a job posting.

Suppose a job description emphasizes "Experience managing cross-functional SOC2 security audits". The candidate forgot to include SOC2 on their current one-page resume because space was tight. However, their master career knowledge base contains an old performance review mentioning that they coordinated SOC2 compliance controls two years ago.

The RAG retriever immediately surfaces that buried achievement chunk, prompting the user: "The employer requires SOC2 experience. We found your SOC2 compliance leadership at your previous company. Would you like to feature this achievement in your tailored resume?"

You can implement a functional RAG resume assistant using Next.js and PostgreSQL with pgvector.

Store candidate master profile entries in a `career_chunks` table. When the user pastes a target job description, chunk the job description by paragraph and run a cosine similarity query against the candidate’s chunks. Rank the requirements by match confidence, and use an LLM with structured output to format an ATS-optimized resume.

• Enforce strict negative constraints in the prompt: "Never invent achievements or metrics not present in the master chunks"
• Use hybrid search (BM25 + pgvector) to match both broad role concepts and exact acronyms (like AWS, HIPAA, or CI/CD)
• Generate personalized, tailored cover letters that cite specific candidate milestones relevant to the employer’s mission
• Provide side-by-side verification: show the user the source chunk next to each generated bullet point for final review