Automatic Maintenance of Technical Documentation Using Large Language Models: A Graduation Project Analysis

This graduation project was developed by viki-terebova and published on May 31, 2026. The GitHub repository link is https://github.com/viki-terebova/Diploma-thesis. The core goal of the project is to use large language models (LLMs) to address pain points in technical documentation maintenance, enabling automatic document update, optimization, and maintaining timeliness and accuracy. The project builds an LLM-driven automation pipeline covering change detection, context understanding, document generation and update, and quality verification, aiming to shift documentation maintenance from passive response to proactive update, improve team efficiency, and enhance user experience.

In the software development field, technical documentation maintenance faces many challenges:

1. Update Lag: Developers prioritize code, so document updates are often delayed or forgotten;
2. Consistency Issues: Multi-person collaboration easily leads to inconsistent styles and terminology;
3. Knowledge Silos: Key knowledge is insufficiently accumulated and only exists in the minds of individual developers;
4. Multilingual Barriers: International products need to maintain multilingual versions, doubling the workload;
5. Complex Formats: Different platforms (Wiki, Markdown, API documentation) have varying format requirements. Statistics show that many teams spend a lot of time writing documents, but they become invalid within months due to code changes. This project targets these pain points and explores the automated application of LLMs.

The project builds a complete automation pipeline consisting of four modules:

1. Change Detection and Trigger: Monitor code commits via Git hooks or CI/CD, identify changes in APIs, function signatures, etc., and trigger updates after evaluating the impact;
2. Context Understanding and Information Extraction: Analyze code differences, parse comments and commit messages, track dependencies, and generate sample code;
3. Document Generation and Update: Automatically rewrite content, generate change logs, provide version migration guides, and convert formats;
4. Quality Verification and Manual Review: Check consistency, evaluate readability, and ensure quality through manual review workflows.

Key technical highlights of the project include:

1. RAG-Enhanced Generation: Combine internal enterprise code repositories, historical documents, and other knowledge bases to ensure the accuracy and coherence of generated content;
2. Multi-Agent Collaboration: Code analysis, writing, review, and translation agents work collaboratively via frameworks like LangChain;
3. Incremental Update Strategy: Precisely locate paragraphs that need updates, keep unchanged parts stable, and support version rollback.

This solution has significant value in the following scenarios:

• API Documentation Maintenance: Automatically generate accurate API reference documents from code comments;
• Developer Documentation Update: Reduce the documentation maintenance burden of framework/library version iterations;
• Enterprise Internal Knowledge Base: Ensure the uniformity and timeliness of scattered documents;
• Open-Source Project Documentation: Help small teams manage documentation for large projects.

The project faces the following challenges:

1. Depth of Semantic Understanding: The model may not fully understand the deep meaning of complex business logic;
2. Creative Content: Generation of creative content such as design decisions and architecture selection still has limitations;
3. Domain Specialization: Highly specialized fields (e.g., medical, aviation) require extensive domain adaptation;
4. Security and Privacy: Strict data desensitization and access control are needed when processing code repositories.

This project demonstrates the practical application of LLMs in software engineering, improving team efficiency through automated documentation maintenance. In the future, with the development of multimodal models and agent technologies, document systems may expand to automatically generate video tutorials, interactive demos, and even optimize structures based on user feedback, transforming from static references to dynamic adaptive intelligent knowledge systems.