A developer has introduced EigenCV—an Infrastructure as Code (IaC) approach for creating professional documents using AI that eliminates the risk of factual distortion. The system uses a Zero-Trust approach, where the AI acts only as an orchestrator for data stored in an immutable format.
What Happened
The EigenCV project has been presented, implementing a resume creation pipeline that separates the data storage process from the generation process. The system uses an immutable database in JSON/Markdown formats, and a deterministic method via LaTeX is applied to assemble the final document. Special attention is paid to the EigenTruth Engine (Lie Detector) mechanism, which interrupts the process if the AI attempts to include information in the text that is missing from the original database.
Context
Traditional approaches to document automation via LLMs often face the problem of hallucinations, where the model invents non-existent skills or experience. EigenCV proposes a shift from purely generative methods to agentic workflows, where the AI is not the source of truth but merely manages verified facts through an Infrastructure as Code architecture.
Why It Matters for the Industry
The project demonstrates a working pattern for solving the hallucination problem in critical tasks where data accuracy is a priority. This paves the way for creating highly reliable documentation automation systems in fields such as medicine, law, and professional reporting, replacing unpredictable text generation with deterministic assembly.
Why It Matters for Users
Users can utilize modern AI agents, such as Cursor or Claude Code, to quickly adapt resumes to specific job openings while maintaining 100% information accuracy. The use of LaTeX guarantees perfect readability of documents for Applicant Tracking Systems (ATS).
Sources
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