I Built a Zero-Trust Resume Pipeline to Stop AI from Hallucinating

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Stop letting ChatGPT hallucinate skills you don't have.

A production-grade Infrastructure-as-Code (IaC) pipeline for generating ATS-perfect, highly tailored LaTeX resumes without sacrificing your integrity.

🤯 Commercial AI Builders vs. EigenCV

The Industry Standard (Commercial AI Builders):

You tell an AI to "optimize my resume for this job." The AI treats your resume as a creative writing exercise. It quietly hallucinates skills, inflates job titles, and paraphrases your engineering achievements into generic HR buzzwords. The result is a PDF that beats the ATS but fails the technical interview because it's full of lies.

The EigenCV Approach (Zero-Trust):

We treat your career history as an immutable database. The AI is strictly an orchestration layer. It does not write your resume; it queries your database to pull the most relevant, pre-verified bullet points.

If the AI attempts to go rogue and hallucinate a missing skill into your profile to artificially boost your ATS score, the Python compiler's Lie Detector intercepts it and hard-crashes the build. Obvious lies never make it into the PDF.

🖼️ Example Output & Gallery

🎨 Total Customization & Control

The sample above is just one configuration. EigenCV puts you in complete control:

Dynamic Sections: Not all sections are mandatory. Choose exactly what to include and reorder them instantly (e.g., move 'Education' to the top) by simply editing the comma-separated cvorder variable.

Change Layouts: Seamlessly swap between professional LaTeX templates (e.g., Awesome-CV, EigenCV-Modern).

Brand Yourself: Inject custom corporate accent colors to match the company you're applying to.- Multilingual: Generate applications in English, German, or any other language using the built-in translation matrix.

🚀 How to Use EigenCV: Choose Your Path

Path 1: The "No-Code" Lifehack (For Non-Coders / ChatGPT Plus)

You don't need to know Python, LaTeX, or Git to use EigenCV. You can run the entire pipeline directly in ChatGPT.

Download this entire repository as a ZIP file.

Build your Master Database: Upload the ZIP to ChatGPT (requires Advanced Data Analysis) or Claude, along with ALL your old resumes. Tell the AI: "Extract all my career facts from these resumes and populate the EigenCV JSON database."

Apply to a Job: Paste the Job Description and tell the AI: "Please apply to this job using the instructions found in docs/AI_CLOUD_PROMPT.md."

Download your PDF: ChatGPT will automatically match your database to the job, generate the JSON, and run the chatgpt_run.py wrapper script. Because we include a dedicated "Cloud-Safe" LaTeX template, ChatGPT will render the PDF directly in its sandbox and give you a download link! (Fallback: If ChatGPT times out, it will still generate the .tex code. You can drag & drop that code into a free Overleaf account for instant rendering (or render the pdf locally using the pdflatex)).

Path 2: The Agentic Developer Route (CLI & IDE)

If you prefer terminal workflows, need maximum build speed, or require strict data privacy, run the pipeline locally.

Prerequisites: You need Python 3.11+ and a LaTeX distribution (e.g., TeX Live or MiKTeX). Alternatively, simply open this repo in VS Code and click "Reopen in Container" to use our pre-built Docker environment!

Install Dependencies: Run pip install -r requirements.txt.

Agent Setup:

For Maximum Speed: Open the repository using an Agentic CLI (like Antigravity / Claude Code) or an Agentic IDE (Cursor / Windsurf) connected to their standard cloud APIs.

For 100 % Hardcore Privacy: Point your Agentic IDE to a local model (like Ollama or LM Studio) so your data NEVER leaves your machine.

Build the DB: Tell the Agent: "Migrate my old CV. Follow AI_START_HERE.md." to build your Zero-Trust database.

Apply: Paste a Job Description and say: "Apply to this job. Follow AI_START_HERE.md."

Automation: The Agent will automatically route the prompts, generate the strict JSON, and execute the Python scripts locally to render your PDF and calculate your ATS score!

⚙️ Architecture & Workflow

Most AI tools use a "generate and pray" approach. EigenCV uses Agentic Determinism. Here is how we guarantee a flawless, hallucination-free application:

The Immutable Master Database: You don't paste your resume into a chat window. Your career history lives offline as a structured JSON/Markdown database on your hard drive. Every achievement, project, and skill has a mathematically verifiable ID (e.g., proj_aws_migration).

AI Orchestration (The Brain): When applying for a job, the AI reads the Job Description and acts as a strategic orchestrator. Instead of writing text, it executes a precise query against your database, returning only the IDs that maximize your ATS match score. It crafts a hyper-authentic Cover Letter based exclusively on your verified profile data.

Deterministic Compilation (The Muscle): The AI is entirely locked out of the rendering process. The EigenCV Python compiler takes the approved list of IDs, fetches the exact text from your offline database, and deterministically injects it into a stunning, ATS-optimized LaTeX template. Flawless layouts, no hallucinations.

sequenceDiagram autonumber actor U as 👤 User participant LLM as 🧠 Agent participant DB as 🗄️ Database participant Py as 🐍 Python Tools

rect rgb(40, 40, 60) Note over U,DB: Phase 0: Setup & Privacy U->>Py: Run scrub_data.py Py->>DB: Wipe private data U->>LLM: Upload old CV LLM->>DB: Build JSON Database end

rect rgb(20, 40, 20) Note over U,Py: Phase 1: Routing U->>LLM: Provide Job Description LLM->>DB: Query database DB-->>LLM: Return valid IDs LLM->>LLM: Generate build_config.json end

rect rgb(60, 40, 20) Note over LLM,Py: Phase 2: Compile LLM->>Py: Execute cv_compiler.py Note over Py,DB: 🏥 Healer auto-corrects IDs 🛡️ Lie Detector prevents hallucinations Py->>DB: Fetch verified text Py-->>LLM: Render PDF via Jinja2 end

rect rgb(40, 60, 40) Note over LLM,Py: Phase 3: Verification LLM->>Py: Execute check_ats_score.py Py->>Py: Parse compiled PDF Py-->>LLM: Return ATS Score LLM-->>U: Present Final PDF & Score! end

Loading

🚀 The "Lie Detector" in Action

+----------------------------- EigenCV Compiler ------------------------------+ | Compiling CV from build_config.json... | +-----------------------------------------------------------------------------+ Using layout template: eigencv_resume.tex.j2, Locale: en +--------------------------- Zero-Trust Violation ----------------------------+ | Zero-Trust Violation: You declared 'Rust' as a missing skill, but it was | | hallucinated into the CV output! | | You cannot artificially inject skills you do not have into free-text fields.| +-----------------------------------------------------------------------------+ ValueError: Hallucinated skill detected: Rust

The user removes the hallucinated skill and recompiles:

+----------------------------- EigenCV Compiler ------------------------------+ | Compiling CV from build_config.json... | +-----------------------------------------------------------------------------+ Successfully compiled CV to CV-Applicant-Google.tex Auto-compiling PDFs with pdflatex... Successfully compiled CV-Applicant-Google.pdf

ATS Match Score: 85.0 % +-------------------------------------------------------------------+ | Category | Skills | |-----------------+-------------------------------------------------| | Missing (1) | Rust | +-------------------------------------------------------------------+ [!] ATS Penalty Applied: 1 critical gap identified.

✨ Core USPs

🛡️ Zero-Trust & The EigenTruth Engine: Your career history lives in a static JSON database. If the LLM attempts to hallucinate a skill you don't have into your profile to artificially boost your ATS score, the compiler's EigenTruth Engine (our Lie Detector) catches it and hard-crashes the build.

🔒 Immutable Database: Your bullet points and skills are strictly IMMUTABLE. You can maintain them yourself or use LLMs to prep them, but within the EigenCV pipeline, the AI is only allowed to select them, never rewrite them.

✍️ Authentic Cover Letters: The AI uses your personal_dossier.md to write hyper-authentic cover letters based only on your real soft skills and hobbies, eliminating corporate fluff.

🎨 Corporate Auto-Coloring: The AI automatically deduces the target company's corporate identity and dynamically injects matching accent colors into the LaTeX output (or you can override it manually).

🧮 Advanced ATS Engine & Reality Checks: The post-compilation Python parser calculates a mathematically honest ATS keyword match score. Meanwhile, the AI Agent acts as a ruthless filter, estimating interview/offer probabilities and salary ranges based strictly on your verified skills, creating a realistic Probability Matrix.

🏥 Self-Healing IDs: If the LLM makes a minor typo when selecting an ID from your database (e.g., aws_mig instead of aws_migration), the compiler's built-in rapidfuzz heuristics will auto-heal the ID, preventing brittle build crashes.

📄 Automated LaTeX Compilation: No more broken LaTeX parsing or missing brackets. The AI generates a strictly typed Pydantic JSON schema, deterministically compiled into beautiful Jinja2 LaTeX templates.

🌍 Multi-Language Support & Auto-Translation (Beta): Applying abroad? The system supports native multi-language CVs with strict language mismatch prevention, and features an experimental auto-translation engine to dynamically localize your database.

🏗️ Dynamic Section Routing: Don't have any open-source projects for a specific application? Simply omit the array in the JSON. The Jinja2 engine will dynamically hide the section and recalculate the LaTeX geometry without leaving awkward whitespace.

🐳 Containerized Reproducibility: Comes with a pre-configured VS Code DevContainer. Boot a fully sandboxed environment to get a full TeX Live distribution inside Docker. (Note: The initial Docker build downloads the 4GB distribution, grab a coffee. After that, compile CVs locally without polluting your host machine.)

🕵️ 100 % Local & Privacy-First (Optional): Your career data never leaves your machine unless you explicitly send it to an LLM via your trusted API or Agent. No web services, no data harvesting.

🔬 Design Decisions (Under the Hood)

EigenCV prioritizes deterministic reliability over trendy AI complexity. For the engineers reading this, here is a transparent look at the core architectural decisions behind the pipeline:

1. "Pseudo-RAG" (Context Window Routing)

We do not use Vector Databases (Chroma, Pinecone) or traditional RAG embeddings. Why? Because an individual's entire career history (even a 20-year veteran's) is only a few kilobytes of text. It easily fits into a modern LLM's context window. Instead of vector search, we use In-Context Semantic Routing. We feed your entire JSON database to the LLM and prompt it to output an array of bullet_ids that semantically match the Job Description. The LLM acts as the retriever, but the actual text insertion is handled deterministically by Python.

1. The EigenTruth Engine (How We Catch Hallucinations)

Is it really "Zero-Trust" if the AI

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