Increasing compute power primarily improves the processing speed (latency) of the system. However, speed is a performance metric, not a quality metric. A faster system does not inherently produce more relevant or accurate content. The relevance of the output is determined by the quality of the underlying model, the retrieval-augmented generation (RAG) process, and the specific algorithms used for relevance analysis, not the speed at which the computation is performed. A system can generate an irrelevant answer very quickly.

A. A guardrail framework with finance-specific content filters is a direct mechanism to ensure outputs stay on-topic and are relevant to the financial domain.

C. A profanity filter ensures the output's tone is appropriate for a professional context like financial news, which is a facet of maintaining domain-specific relevance and quality.

D. Manual reviews (human-in-the-loop) provide a direct and highly effective method to check, correct, and guarantee that every output is relevant before it is delivered to the end-user.

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1. Databricks Documentation, "AI Guardrails with Databricks Lakehouse Monitoring": This document explains how to implement guardrails to control model outputs. It explicitly mentions "topical guardrails" which "check whether a model’s output is on topic." This directly supports the effectiveness of option A for ensuring relevance. It also discusses quality guardrails, which can encompass filters like those in option C.

2. Databricks Documentation, "Monitor quality and drift for LLMs": This guide details the metrics for monitoring LLM applications. It clearly separates performance metrics like latency from quality metrics like relevance (defined as "The model's responses are relevant to the query and the provided context"). This separation confirms that improving compute for speed (latency) does not directly improve content relevance.

3. Databricks Documentation, "Create a training dataset for model fine-tuning using Dolly": This page describes the process of collecting human-generated instruction-following records. This principle of using human judgment to create high-quality data is analogous to manual review (Option D) for correcting and ensuring the relevance of model outputs in a production system.