🤔Is Fine-Tuning Right for Me?

If you're stuck on if fine-tuning is right for you, see here!

When deciding between using RAG or fine-tuning, it's important to note that fine-tuning can replicate RAG's functionalities and not vice versa. Instead of using one or the other, we recommend people to use both which greatly increases accuracy, useability and reduces hallucinations.

Benefits of Fine-tuning

1. Fine-tuning can do everything RAG can, but RAG can't Fine-tuning can replicate RAG’s functionality by embedding external knowledge directly into the model during training, allowing it to perform tasks like answering niche questions or summarizing documents without relying on external systems. Fine-tuning can also integrate context and patterns into the model, mimicking retrieval behavior.

2. Task-Specific Mastery Fine-tuning embeds deep knowledge of a domain or task directly into the model, enabling it to handle structured, repetitive, or nuanced queries with high accuracy - something RAG alone cannot achieve.

3. Independence from Retrieval A fine-tuned model operates effectively without external data, ensuring seamless performance even when the retrieval system fails or the knowledge base is incomplete.

4. Faster Inference Fine-tuned models provide direct responses without needing a retrieval step, making them ideal for scenarios where speed is critical.

5. Custom Behavior and Tone Fine-tuning allows for precise control over how the model behaves and communicates, ensuring alignment with brand voice, regulatory requirements, or other constraints.

6. Fallback Robustness In combined systems, the fine-tuned model ensures a baseline level of reliable task performance, even if the RAG system retrieves irrelevant or incomplete information.

Combining RAG + Fine-tuning

RAG enhances adaptability by dynamically accessing external knowledge, while fine-tuning fortifies the system's core expertise, ensuring it performs reliably without over-relying on retrieval. Moreover, fine-tuning empowers the model to interpret and integrate retrieved information more effectively, producing seamless and contextually accurate responses.

Why should you combined RAG & fine-tuning?

• Task-Specific Expertise: Fine-tuning excels at specific tasks, while RAG dynamically retrieves up-to-date or external knowledge. Together, they handle both core and context-specific needs.

• Adaptability: Fine-tuned models provide robustness when retrieval fails, and RAG enables the system to stay current without constant re-training.

• Efficiency: Fine-tuning establishes a baseline, while RAG reduces the need for exhaustive training by handling dynamic details.