RAG คืออะไร | ทำยังไงให้ LLM ถามอะไรก็ตอบได้ (และไม่ใช้ความรุนแรง)

Introduction

This tutorial explains Retrieval-Augmented Generation (RAG), a technique that enhances the performance of Large Language Models (LLMs) by incorporating external knowledge. RAG is particularly useful for applications requiring personalized or customized responses, improving accuracy and reducing errors known as hallucinations.

Step 1: Understand What RAG Is

RAG stands for Retrieval-Augmented Generation. It combines the capabilities of LLMs with external data retrieval mechanisms. This helps the model to:

• Access relevant information beyond its pre-existing knowledge.
• Provide more accurate, context-aware answers.
• Minimize the risks of hallucination, where the model generates incorrect information.

Key Points

• RAG relies on relevant documents or context to enhance responses.
• The technique is essential when the model's inherent knowledge may not suffice for specialized queries.

Step 2: Identify Relevant Documents

To effectively use RAG, it’s crucial to determine what constitutes a relevant document. These documents can include:

• Articles or research papers related to the topic.
• FAQs or knowledge bases that provide specific answers.
• User-generated content that reflects real-world experiences and questions.

Practical Advice

• Ensure the documents are up-to-date and credible.
• Use a variety of sources to broaden the context available to the model.

Step 3: Integrate Context into LLM Responses

Once you have identified relevant documents, the next step is to integrate this context into the LLM’s responses. This can be done by:

• Feeding the model both the question and the relevant document excerpts.
• Structuring prompts that clearly indicate what information is relevant.

Example Prompt Structure

Question: What is PakapongZa?
Context: [Insert relevant document or excerpt here]

Step 4: Test and Iteratively Improve

After implementing RAG, it’s essential to test the model's responses and make adjustments as necessary. You can:

• Evaluate the accuracy of the answers against known facts.
• Gather user feedback to identify areas of improvement.
• Experiment with different sources of context to see which yields the best results.

Common Pitfalls to Avoid

• Relying too heavily on outdated or irrelevant sources can mislead the model.
• Overloading the model with excessive context may confuse it, leading to inaccurate responses.

Conclusion

Retrieval-Augmented Generation is a powerful technique for enhancing LLM applications by providing them with relevant and contextual information. By understanding RAG, identifying pertinent documents, integrating context effectively, and continuously testing and improving your approach, you can significantly enhance the quality of responses generated by LLMs. Consider exploring various sources and maintaining an iterative process to refine your application further.