Development of Large Model Applications: Principles and Best Practices of RAG Technology

Development of Large Model Applications: Principles and Best Practices of RAG Technology

RAG (Retrieval Augmented Generation) is currently the most mainstream technical solution for the implementation and application of large models.

1、 Basic principles of RAG

• Index stageSplit the document into fragments, convert them into vectors using the Embedding model, and store them in a vector database
• Search phaseConvert user questions into vectors and retrieve the most relevant document fragments
• Generation stageUsing the retrieved document fragments as context, input them into the large model to generate answers

2、 Key technical points

1. Document segmentation strategySplitting by semantics rather than fixed length
2. Embedding model selectionChoose the appropriate vectorized model based on language and domain
3. Search strategy optimizationMixed retrieval and reordering (Reranker)
4. Prompt EngineeringDesign a reasonable Prompt guidance model

3、 Advanced optimization

• Multi channel recall: combining multiple retrieval strategies to improve recall rate
• Query rewriting: Rewrite and expand user questions
• Context compression: removing redundant information from the retrieved content
• Answer verification: Conduct factual verification on the generated answer

RAG technology enables large models to leverage enterprise private knowledge and is the core technology solution for enterprise level AI applications.