Education Policy Q&A System Based on RAG Architecture: Practice of Beca 18 Intelligent Assistant

This article introduces an end-to-end Retrieval-Augmented Generation (RAG) intelligent Q&A system for Peru's Beca18 scholarship policy, aiming to solve the problem that official regulatory documents are lengthy and complex, making it difficult for ordinary applicants to quickly obtain key information. The system combines document retrieval and large language models to provide accurate policy consultation services, with advantages such as factual accuracy, traceability, flexibility in knowledge updates, and cost-effectiveness.

In the field of education policy, obtaining accurate information often faces many challenges. Taking Peru's Beca18 scholarship program as an example, official regulatory documents are long and complex, making it difficult for ordinary applicants to quickly locate key information; traditional keyword search is inefficient, and manual customer service has response delays and cost issues. To address this pain point, developers have built an end-to-end RAG system specifically for answering questions related to Beca18's official policies.

Retrieval-Augmented Generation (RAG) is a technical architecture that combines information retrieval and text generation: when a user asks a question, the system first retrieves relevant document fragments from the knowledge base, then provides these fragments as context to the large language model to generate fact-based answers. Compared to using large language models directly, RAG's advantages include: factual accuracy (reducing hallucinations), traceability (answers can be traced back to source document fragments), flexible knowledge updates (no need to retrain the model), and cost-effectiveness (avoiding the high cost of domain fine-tuning).

The RAG pipeline of this project includes three key components: 1. Document processing layer: Parsing Beca18's official PDF documents and chunking them—chunking strategy directly affects retrieval quality (too small chunks may lose context, too large chunks may introduce irrelevant information); 2. Vector storage and retrieval: Converting document chunks into vectors and storing them in a database; after vectorizing the user's question, searching for semantically similar fragments in the vector space; 3. Large language model interaction: Submitting the retrieved relevant fragments as context along with the question to the model to generate answers consistent with official policies.

Such RAG systems are widely used in the public service field: education policy consultation (helping students and parents quickly understand scholarship application conditions, processes, etc.), government service windows (24/7 online policy consultation reduces manual pressure), enterprise internal knowledge bases (Q&A on rules and regulations), legal document query (assisting in locating legal provisions and cases).

Building a production-level RAG system requires considering: 1. Embedding model selection (choosing an appropriate model based on the domain); 2. Retrieval optimization (improving result relevance through re-ranking); 3. Prompt engineering (designing effective prompts to guide the model to answer accurately); 4. Evaluation system (establishing a Q&A quality evaluation mechanism to monitor performance).

The Beca18 RAG Chatbot project demonstrates the practical application of large language models in public service scenarios, achieving an accurate and smooth interactive experience by combining the RAG architecture. With the development of large model technology, RAG will show value in more fields; for developers, mastering RAG system design and implementation is an important skill for large model application development.