Fine-Tuning vs. Knowledge Bases (RAGs)

Fine-tuning and RAG offer distinct advantages, but choosing one over the other can significantly impact your AI system’s effectiveness. In this guide, we’ll explore the challenges enterprises face when optimizing AI models, examine the technical differences between fine-tuning and RAG, and provide a clear roadmap for making the right decision.
How Forage AI Combined Fine-Tuning & RAG for Reputation Intelligence

At Forage AI, we implemented a hybrid AI approach—leveraging both fine-tuning and Retrieval-Augmented Generation (RAG)—to help a client track and assess reputations on Key Opinion Leaders (KOLs) across news and social media in real-time. The goal was to determine whether any positive or negative mentions about an expert could impact their professional standing.

Fine-Tuning for Context-Aware Sentiment Analysis

One of the biggest challenges with standard LLMs is that they often provide generic sentiment classifications without considering the nuances of individual cases. When asked whether an article was “positive” or “negative,” the model would simply classify it based on overall tone, ignoring how it applied to the specific expert mentioned.

For example:

• A medical expert might be cited in an article about a failed procedure or a controversial study with adverse outcomes. While the overall sentiment of the article was negative, the expert might have only played an advisory role or been the author of the study—not the cause of the failure.

• Conversely, an expert might be featured in a positive article—winning an award or excelling in a personal hobby—but this would not be relevant to their professional reputation.

To address this, we fine-tuned the LLM to:

• Distinguish between general sentiment and expert-specific sentiment

• Identify whether the expert was passively mentioned or actively involved

• Weigh professional relevance over generic positivity or negativity

Fine-tuning ensured that reputation analysis was aligned with the client’s specific use case rather than relying on surface-level sentiment classifications.

RAG for Real-Time Data Feeds

While fine-tuning improved sentiment classification, it was only effective if the latest information was available. This is where RAG came into play.

We built a real-time ingestion pipeline to continuously fetch new data from news articles, social media platforms, and other authoritative sources. This allowed the system to:

• Pull fresh insights on experts as soon as new articles or posts were published

• Maintain a live knowledge base without frequent fine-tuning retraining

• Ensure AI-driven assessments always reflected the most current developments

By combining fine-tuning with RAG, we ensured that reputation assessments were both highly accurate and dynamically updated.