Rapidly Build a Labor Law AI Q&A Assistant Using Claude and Coco AI

Problem Background

Legal department needed efficient retrieval of scattered labor law statutes and case files; keyword search was inefficient and a complex labor dispute highlighted the need for an intelligent Q&A system.

Solution Exploration

Self‑built RAG system

Advantages: strong controllability and deep customization. Technical path: use Elasticsearch or Milvus as a vector database, process documents with Python scripts to generate embeddings, and integrate GPT or another large language model for the RAG architecture. Drawbacks: requires handling document parsing, vectorization, retrieval ranking, and model integration; estimated development cycle 1–2 months; significant infrastructure effort.

Coco AI toolkit

Open‑source Coco AI provides an end‑to‑end solution for document processing, vector retrieval, and AI Q&A, supports multiple file formats, and uses a plugin‑based design for rapid local document ingestion. Chosen to validate an MVP quickly and focus on business value rather than low‑level implementation.

Practical Implementation

Environment setup

Created a “Labor Law Assistant Connector” to prepare the data source, linked the connector to the document repository, and configured a localized large language model to enable local intelligent Q&A.

Data preparation

Saved the full text of the Labor Law as labor_law.txt. A Python script parsed the document, identified 13 chapters and 107 individual articles, and wrote each article to a separate file named Chapter_X_Article_Y.txt with metadata (chapter information, creation time). The script then bulk‑imported the 107 files into Coco AI via its API, creating a structured local document library searchable by the system.

Verification and Optimization

Full‑text search test

Querying “解除劳动合同” returned relevant statutes and cases within one second.

RAG‑based Q&A test

Sample questions such as “What does Article 41 of the Labor Law say?” and “Which clauses cover overtime pay?” retrieved the correct articles. Real‑world use revealed issues: inaccurate handling of specialized terms (e.g., “N+1 compensation”) and occasional hallucinations (answers not matching actual statutes). Prompt templates and retrieval strategies were iteratively refined to improve answer relevance.

Business scenario application

Legal staff used the assistant to quickly locate arbitration cases and procedural guidelines, improving efficiency. HR teams leveraged it to verify compensation policies against the latest regulations. Continuous feedback and prompt adjustments increased answer accuracy over time.

Summary

The project demonstrates that RAG technology combined with the Coco AI toolkit can automate structuring of complex legal texts, create a searchable knowledge base, and deliver accurate AI‑driven answers. The workflow—from a single Python script that parses 107 articles to bulk import and interactive querying—provides a reproducible solution for future legal‑document automation.