Ever since the technique of retrieval augment generation (RAG) came to the forefront of discussions, people have been wondering about the need to fine-tune AI models on their own data. Since both these methods are meant to improve an LLM’s knowledge with new data, it’s important to know when to use what.
Most people believe that RAG makes more sense when trying to retrieve more information and doing keyword searches, which is true. The problem is that it does not eliminate the need for heavy computing as much as pre-training does, but remains a cheaper alternative.
What do experts say?
Microsoft published a research paper comparing both the techniques during a case study in agriculture. Despite its computational intensity in the retrieval phase, RAG shone in tasks necessitating profound contextual understanding.
Another research paper by Microsoft highlighted that RAG was a more reliable choice regarding knowledge injection, while fine-tuning performed better for brevity and inputting style in the LLM when using synthetic data.
RAG sifts through extensive datasets to extract pertinent context or facts. This contextual repository subsequently informs the sequence-to-sequence model, enhancing the richness and relevance of generated outputs. Despite its computational intensity during the retrieval phase, RAG shines in tasks necessitating profound contextual understanding.
Fine-tuning also demands substantial computational resources, especially when employed on complex models or from scratch.
- Speed and Latency: RAG experiences slightly higher latency than fine-tuning due to the two-step process but excels in context-heavy tasks. This makes fine-tuning ideal for real-time applications like chatbots.
- Scalability: The generation component of RAG is scalable; however, the retrieval component may demand substantial computational resources.
- Performance Metrics: RAG outperforms in accuracy and context richness, particularly for complex tasks requiring external information. Fine-tuning, on the other hand, often exhibits superior performance in specialised tasks, evident through metrics like accuracy and F1 score.
Does RAG actually beat fine-tuning?
Pascal Biese says that fine-tuning is still in the picture, but in terms of efficiency, RAG might be a better choice. “While both methods significantly improved the handling of niche information in question-answering tasks, it was RAG that led the charge, with fine-tuning not far behind,” he said.
Choosing between RAG and fine-tuning hinges on the specific requirements of the application. Factors such as access to external data, the need for behaviour modification, and the dynamics of labelled training data play pivotal roles in this decision-making process. But as experts believe, RAG is outperforming fine-tuning on various LLM applications as the day progresses.
Firstly, access to extensive external knowledge bases leads to more accurate responses. Secondly, incorporating factual external information minimises errors in generated responses, reducing hallucinations and biases. Thirdly, RAG easily adjusts to evolving information, making it ideal for tasks requiring up-to-date knowledge. Additionally, responses traceable to referenced knowledge sources enhance interpretability and transparency, aiding in quality assurance.
Some use cases for RAG systems include accessing current medical documents helping professionals in accurate diagnosis and treatment recommendations. Moreover, RAG models expedite legal document analysis, improving accuracy and efficiency in legal processes, which are some of the use cases.
On the other hand, fine-tuning optimises LLM performance for specific tasks, such as:
- Pre-train a general-purpose LLM on extensive text and code to learn language patterns.
- Collect and label a dataset relevant to the desired task.
- Ensure data quality and consistency by correcting errors and removing duplicates.
- Modify LLM layers based on task-specific data to optimise performance.
- Set parameters for fine-tuning to achieve optimal results.
- Feed reprocessed data to the LLM and train using a backpropagation algorithm.
- Assess LLM performance on unseen data to ensure desired task completion.
- Repeat fine-tuning steps to improve performance before deploying the model.
Fine-tuning enhances LLM capabilities for various applications. It improves sentiment analysis by enhancing understanding of text tone and emotion, aiding in accurate analysis of customer feedback. Additionally, it enables LLMs to identify specialised entities in domain-specific text, enhancing data structuring. Moreover, fine-tuning customises content suggestions based on user preferences, fostering engagement.
Giving an example of pricing of GPT-3.5, Santiago said, “99% of use cases need RAG, not fine-tuning,” as fine-tuning of the model is more expensive, and both are for different purposes.
The best way forward
A lot of people said that RAG would make fine-tuning obsolete. But it was the same set of people who proclaimed that the launch of LLMs with larger context windows, such as Claude-3, would make RAG obsolete. But both of those are still alive and exist as viable alternatives.
Considerations for choosing between RAG and fine-tuning include dynamic vs static performance, architecture, training data, model customisation, hallucinations, accuracy, transparency, cost, and complexity. Hybrid models, blending the strengths of both methodologies, could pave the way for future advancements. However, their implementation demands overcoming challenges such as computational load and architectural complexity.
While fine-tuning remains a viable option for specific tasks, RAG often offers a more comprehensive solution. With meticulous consideration of nuances and contextual requirements, leveraging RAG augmented by prompt engineering emerges as a promising paradigm.
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