Building a Retrieval-Augmented Generation System Based on Large Language Models: Technical Practice to Solve AI Hallucination

Core Points: This article deeply explores the architectural design and implementation methods of Retrieval-Augmented Generation (RAG) systems, analyzing how combining external knowledge bases with large language models can effectively mitigate the problem of model hallucination and improve the accuracy and verifiability of generated content.

Original Author and Source:

• Original Author/Maintainer: pratikgaikar2903
• Source Platform: GitHub
• Original Title: -LLM-Powered-Document-Retrieval-System-RAG-
• Original Link: https://github.com/pratikgaikar2903/-LLM-Powered-Document-Retrieval-System-RAG-
• Source Publication/Update Time: 2026-06-11T00:04:15Z

Large Language Models (LLMs) exhibit amazing text generation capabilities, but they have long suffered from the problem of model hallucination: when dealing with professional knowledge outside training data, internal enterprise documents, or real-time information, they tend to generate content that seems reasonable but is actually incorrect.

Retrieval-Augmented Generation (RAG) technology provides a systematic solution to this problem: by introducing an external knowledge retrieval mechanism during the generation process, the model can answer based on real, verifiable information instead of relying solely on internal parameterized knowledge.

The core idea of RAG systems is the three-step process of "Retrieve-Fuse-Generate":

1. After receiving a user query, retrieve relevant document fragments from the knowledge base;
2. Fuse the retrieved content with the original query;
3. The language model generates an answer based on the enhanced context.

Advantages:

• Compared to fine-tuning: No need to retrain the model, low cost for knowledge updates;
• Compared to prompt engineering: Can handle massive documents far beyond the model's context window.

Steps for knowledge base construction and document indexing:

1. Document loading and parsing: Support formats like PDF, Word, Markdown; extract text and retain structural information;
2. Text chunking: Split long documents into small fragments; common strategies include fixed-length, paragraph-based, and semantic boundary-based chunking;
3. Vectorization: Convert text chunks into high-dimensional vectors using pre-trained embedding models (e.g., text-embedding-ada-002, Sentence-BERT);
4. Index storage: Store vectors in vector databases (e.g., Pinecone, Weaviate, Milvus, FAISS) and build approximate nearest neighbor indexes to support fast retrieval.

Retrieval mechanism process:

1. Query vectorization: Convert user queries into vectors using the same embedding model;
2. Similarity search: Find the K closest document chunks in the vector database; key choices include similarity metrics (cosine similarity, Euclidean distance) and retrieval parameters;
3. Hybrid retrieval strategy: Combine vector retrieval with traditional keyword retrieval (e.g., BM25) and refine candidate results via re-ranking models; some systems use query expansion techniques to cover potential needs.

Context fusion and generation optimization:

• Direct concatenation: Input retrieved document chunks and queries into the model, but face context length limitations;
• Prompt templates: Explicitly instruct the model to answer based on reference materials and honestly inform if no answer is found;
• Multi-turn dialogue processing: Maintain dialogue history, identify new needs and resolve references to ensure retrieval continuity and accuracy.

Practical application scenarios of RAG technology:

• Enterprise knowledge management: Employees query internal documents, rules, etc., in natural language to get instant and accurate answers;
• Customer service: Intelligent customer service answers user questions based on the latest product documents and policies;
• Scientific research: Quickly retrieve and synthesize academic papers;
• Legal industry: Query cases and regulations to improve case research efficiency.

Conclusion: Retrieval-augmented generation technology represents the evolutionary direction of AI application architecture—from relying on model parameters to collaboration between models and external knowledge. With advances in embedding models, vector databases, and LLMs, the capability boundary of RAG continues to expand, and mastering RAG is an essential skill for developers and enterprises.

Optimization suggestions:

• Improve embedding models to capture domain-specific semantics;
• Adjust chunking strategies;
• Introduce query rewriting technology;
• Use stronger re-ranking models;
• Try advanced technologies like multi-way recall fusion.