RAG is a core component of LLM applications. The idea is to index your data in a vector database and then use the LLM to generate responses based on the indexed data. The concept seems simple but the implementation can be complex. I recently am researching on Dify - a popular LLM application platform and found its RAG engine is a good case study to understand how to implement a comprehensive RAG system.

As Large Language Models (LLMs) become increasingly critical in production systems, robust evaluation frameworks are essential for ensuring their reliability and performance. This article tries to walk you through modern LLM evaluation approaches, examining key frameworks and their specialized capabilities. Core Evaluation Dimensions It’s important to understand that LLM evaluation is not a one-size-fits-all task. The evaluation framework you choose should align with your specific use case and evaluation requirements. In general, there are three core dimensions to consider:

It’s not too hard these days to generate a research report or an article with the help of AI. You just need to figure out the topic and prompt chatgpt, it will generate a draft for you. Potentially you can polish the draft and work towards a decent article. However I found myself even lazier than that. I just want to show the AI the topic and some keypoints and let it generate a long comprehensive article for me.