Introduction to Retrieval-Augmented Generation (RAG)

General language models can perform common tasks such as analyzing emotions and identifying named entities through fine-tuning, which do not require additional background knowledge.

For more complex knowledge-intensive tasks, it is necessary to build a system based on language models to access external knowledge sources. This approach enhances the reliability of answers, improves factual consistency, and helps mitigate the "hallucination" problem.

Working Mechanism of RAG:

• Accepts input and retrieves relevant/supporting documents (e.g., from sources like Wikipedia);
• Combines document content as context with the original prompt and feeds them into a text generator to produce the final output.

Core Advantages:

• Adapts to scenarios where facts change over time (LLM parametric knowledge is static, while RAG can acquire up-to-date information through retrieval);
• Enables knowledge updates without retraining the model, generating reliable outputs based on retrieval.