# For Advanced RAG This section introduces techniques and utilities for creating advanced Retrieval-Augmented Generation (RAG) workflows using RAGchain. In RAGchain, we delve into the complexities of constructing production-level RAG workflows. It's challenging to create a flawless RAG workflow tailored to your specific needs. However, certain patterns can enhance the effectiveness of these workflows, so we prepare patterns that you might need. We are continually developing and improving this framework, so expect further updates. ## Time-Aware RAG In real-world applications, you'll often find that the latest information is more valuable than outdated data. However, manually discarding old information can be challenging and potentially harmful, as this information might be useful for other queries. Therefore, time-aware RAG is crucial for certain applications. Time-aware RAG prioritizes recent information without discarding older data that might still be relevant. To learn how to implement time-aware RAG in RAGchain, please refer to [Time-Aware RAG](/ragchain-docs/advanced-rag/time_aware_rag.md). ## Importance-Aware RAG In some cases, you may want to prioritize certain passages over others. For example, when you try to find specific information at Google search, there are some websites that you trust more than others. Or, some websites that you don't want to see. In this case, you can use importance-aware RAG. Importance-aware RAG prioritizes certain passages over others. To learn how to implement importance-aware RAG in RAGchain, please refer to [Importance-Aware RAG](/ragchain-docs/advanced-rag/importance_aware_rag.md). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://nomadamas.gitbook.io/ragchain-docs/advanced-rag.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.