To Fine-Tune or Not?

1. Introduction

Deciding whether to apply fine-tuning when building an LLM-powered application can be challenging. This guide was inspired by a recent project, where questions about fine-tuning arose. Fine-tuning can be quite daunting for small teams or independent builders with limited resources.

This guide offers a concise, research-driven framework for determining when to apply fine-tuning. It also serves as a personal reference for future projects, helping to assess whether fine-tuning is necessary, which methods have already been explored, or if alternative methods, such as prompt engineering or retrieval-augmented generation (RAG), may be more suitable.

2. Purpose

When navigating the decision to apply fine-tuning in LLM-powered applications, consider the following key questions:

• What approach has been previously explored?
• Why is fine-tuning the next/right approach?
• What prerequisites should be in place before fine-tuning?
• What factors should guide model selection, if necessary?
• What makes fine-tuning suitable for this product use case?
• How should the fine-tuning process be approached?

Rule of Thumb: Start with the simplest approach when building LLM-powered applications and progressively increase complexity based on insights from testing, data analysis, and user feedback until the desired outcome is achieved.

3. Pre-Requisites for Fine-Tuning

Clear pre-requisites can save significant time and effort.

Checklist:

Product Requirements:

Clearly defined requirements provides a sense of direction in comparison to applying an engineering process in hopes of achieving a vague goal

• Define/Clarify product’s core functionality.
• Clarify the specific objectives: What exactly are we trying to achieve?
• Outline 1-2 clearly defined use cases that represent the desired end goal.

Metrics:

Metrics guide decisions on whether to continue iterating on the fine-tuning process or pivot to an alternative approach.
Lack of clearly defined metrics can be detrimental long term.

• Identify measurable success criteria, such as accuracy, response relevance, or latency as applicable to the product use case.
• Establish a method for tracking these metrics throughout iterations.
• Define clear thresholds or targets that indicate success.

Data:

• How much data is available and can be acquired?
• Define what quality means in terms of data as applicable to the product use case.
• Ensure a continuous pipeline for acquiring high-quality data if needed.
• Prioritize smaller, high-quality datasets over large, noisy datasets.

4. When to Fine-Tune

A structured approach to deciding if fine-tuning is necessary.

Guiding Questions:

• Has prompt optimization with few-shot examples been considered for improved performance?
• Is there a need for a consistent tone or style beyond generic LLM capabilities?
• Does the use case involve domain-specific knowledge (e.g., medical, legal)?
• Are high token costs limiting the current approach?

Guideline: If simpler, iterated approaches like prompt engineering fail to meet requirements, fine-tuning becomes a viable approach, depending on the use case.

Insightful decision-making Flowchart, referenced from Adapting LLMs

5. Model Selection

Trade-offs are important when considering model selection. A generic decision framework is to start with smaller models that meet the product use case. There are more nuances to consider depending on the use case, resource availability, etc. Some models are better suited for specific tasks, so evaluate both the model’s functionality and size relative to the product needs.

Small Models:

• Provide faster performance and lower costs.
• May have limitations in terms of accuracy and contextual understanding.

Large Models:

• Deliver higher accuracy and better performance.
• Require more computational resources and incur higher costs (billions of parameters).

Approach:

1. Evaluate feasibility, analyze results from experiments, and assess costs using smaller models.
2. Gradually scale up to larger models if needed, guided by insights from testing and user feedback.
3. Recall the Rule of Thumb: Start with the simplest approach and increase complexity iteratively.

6. Use Cases Best Suited for Fine-Tuning

• Adapting LLMs to reflect specific personas accurately.
• Delivering domain-specific knowledge (e.g., legal, medical).
• Correcting persistent hallucinations that prompt engineering cannot resolve.
• Tasks that require high precision and customization (e.g., medical diagnosis reports).
• Reducing long prompts by embedding learned behaviors directly into the model.
• Applications that require responses in a specific format or predefined structure (e.g., drafting legal documents with formal language and structured sections).

7. Fine-Tuning Methodologies

Fine-tuning methods can be broadly categorized as:

• Full Fine-Tuning: Updates all model parameters; resource-intensive but ideal for significant tasks or new domains.
• Parameter-Efficient Fine-Tuning (PEFT): Updates a small fraction of parameters; cost-efficient for smaller changes like tone or style adjustments.

Recommendation: Conduct thorough evaluations to determine the most suitable fine-tuning strategy for your use case, with a strong preference for PEFT to optimize cost and efficiency.

8. Fine-Tuning vs. RAG (or Both)

Guidelines:

• Fine-Tuning: Best suited for tasks requiring high customization, domain-specific knowledge, or consistent output formats.
• RAG (Retrieval-Augmented Generation): Ideal for dynamic data needs, real-time updates, and generating responses with citations or references.
• Fine-Tuning + RAG: Combines RAG for retrieving relevant data and a fine-tuned model to maintain tone and structure, offering the best of both worlds.

There are overlapping use cases where it may be unclear which approach best aligns with product requirements. Refer to the references section for additional guidance.

Example:

• For real-time product updates, RAG is ideal.
• For consistent service recommendations in a specific voice or company style, fine-tuning is better.

9. Conclusion

Fine-tuning an LLM can greatly enhance its capabilities to meet specific product requirements. A well-defined checklist of key questions can streamline decision-making and ensure alignment with project goals, saving valuable time and resources. This guide provides a structured framework to assess whether fine-tuning is the appropriate next step for achieving desired outcomes.

10. References