When to Go Out and When to Stay In: RAG vs. Fine-tuning

Language models are here to stay. Their practical uses and impact go beyond simple text generation tasks and question-answering, with user prompts being crafted to address much more complex tasks. Among the various strategies to overcome the limitations and challenges faced by language models, such as hallucinations, data obsolescence, or lack of relevant context, two strategies stand out: retrieval augmented generation (RAG) and model fine-tuning.

This article discusses when to use these two approaches, highlighting scenarios where implementing an RAG system is a more practical and effective solution than fine-tuning the model, and vice versa.

RAG vs. Fine-tuning

 
Whilst both methods can increase the value of language models in daily and organizational contexts and help adapt the language model to a specific application domain, their underlying mechanisms have little in common. Below, we define each solution and list situations where you should opt for one or the other.

When to Use RAG Alongside a Language Model

 
RAG expands language model solutions by incorporating a mechanism called a retriever. The retriever accesses a knowledge base — such as internal data in an organization — containing domain-related documents to help enrich the user query, enriching it with context to get a more accurate and truthful response generated by the language model. RAG adds a layer of sophistication to your language model workflow but normally does not alter the model architecture and parameters.

Below are some situations when incorporating RAG in your language model solution might be worthwhile:

1. You or your team have data science and AI engineering skills, more specifically related to architecting and implementing information retrieval solutions.
2. You need strictly up-to-date information or real-time data access to constantly evolving knowledge bases (e.g., latest news, financial data, or customer-specific information) that would otherwise entail an inadmissibly frequent update of training datasets and constant model fine-tuning.
3. The scope of the task is not narrow enough for efficient fine-tuning, since it involves multiple domains or extensive data with a certain degree of diversity, e.g. news articles spanning a variety of topics.

When to Fine-tune a Language Model

 
Instead of “connecting” a knowledge base with a language model, fine-tuning retrains the model exposing it to a domain-specific dataset (for instance, pharmacology scientific texts in a pharmacy firm), so that the model parameters get updated accordingly. As a result, it becomes more skilled at addressing language tasks in the scope of that domain. Whilst not as highly computationally expensive as training a language model from scratch, depending on the size (number of parameters) of the model itself and the size of the domain-specific data used for fine-tuning, the process may take a moderate to substantial amount of time.

Here are some situations when fine-tuning a language model might be the way to go:

1. You have access to high-quality domain-specific text datasets, as well as data science and AI engineering people/skills with outstanding deep learning model architecting and model fine-tuning capabilities.
2. You have strong computational resources and infrastructure to efficiently undertake the time-consuming process that language model fine-tuning normally entails.
3. The intended application or language use cases require deep domain expertise with significant use of specialized language both in user prompts and generated text, such as medical or legal jargon. Language understanding at a very precise level is critical in these narrow-scope scenarios, and RAG alone may not be reliable enough.

RAG vs. Fine-tuning Comparison

 
The following table summarizes key aspects to consider in application scenarios for opting for RAG or fine-tuning of language models.

RAG vs. Fine-Tuning: When to use each (click to enlarge)

Final Thoughts: What About Hybrid Solutions?

 
How about implementing a hybrid solution that uses RAG but also fine-tunes the language model with a predetermined frequency? That can be a strategic and flexible solution, for two reasons.

First, fine-tuning at certain time intervals ensures the model finely retains domain-specific knowledge, while RAG supplements this with real-time, diverse information that gets updated in the knowledge base in between fine-tuning executions. If planned strategically, a hybrid approach can help optimize resource use, leveraging RAG to manage broad, diverse queries without costly, continuous fine-tuning.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.