The most effective method to mitigate inflammatory outputs in a Retrieval-Augmented Generation (RAG) system is to address the root cause: the content within the knowledge base. The RAG process retrieves information from this upstream data to provide context for the language model's response. If the source documents contain offensive or inflammatory material, the system is likely to retrieve and incorporate it into its output. Proactively curating the data, including manual review and cleaning, ensures that the foundational information used by the RAG system is free from undesirable content. This "Garbage In, Garbage Out" principle is fundamental to building safe and reliable AI systems.

A. Increasing update frequency addresses data staleness, not content quality. It could potentially introduce more unvetted, inflammatory data.

B. Informing the user is a disclaimer, not a technical solution. It manages expectations but does not fix the underlying problem.

C. Restricting access to the source data is an access control measure that does not alter the content the RAG system uses.

1. Databricks Blog, "A Guide to Building High-Quality RAG Applications" (2024). In the section "Step 1: Data Preparation," the guide states, "The first step to building a high-quality RAG application is preparing your unstructured data... This includes cleaning the data to remove irrelevant information (e.g., HTML tags, boilerplate text), checking for and handling PII, and ensuring the data is accurate and up-to-date." This emphasizes that data quality control is the foundational step.

2. Gao, Y., et al. (2024). "Retrieval-Augmented Generation: A Survey". arXiv:2312.10997. In Section 5.3, "Trustworthiness," the paper discusses how the quality of the retrieval corpus directly impacts the final generation's safety. It notes that issues like toxicity and bias are often inherited from the retrieved documents, highlighting the importance of a clean, high-quality knowledge source.

3. Databricks Documentation, "What is Retrieval-Augmented Generation (RAG)?". The documentation explains that RAG "grounds the LLM on a specific set of external knowledge sources." This core principle implies that the content and quality of those external sources directly dictate the boundaries and nature of the model's potential responses. Curating these sources is therefore the most direct way to control output quality.