RAG vs fine-tuning: which AI strategy fits your enterprise?
Generative AI is changing the game for businesses and data teams alike, becoming the backbone of digital transformation across industries. But the real challenge is not about adopting LLMs; it’s making them work for your business. Right now, two main strategies are helping companies get there: Retrieval-Augmented Generation (RAG) and fine-tuning.
But which one is better for your company? And what is the difference between the two? Is it okay to use them together? Or when should you pick RAG vs fine tuning?
Let’s dive into the fundamentals to answer these common questions.
Why is RAG and fine-tuning important for enterprises AI strategy?
Both RAG and fine-tuning solve big challenges with large language models (LLMs).
Without consistent access to new information and data, large language models (LLMs) run into issues like hallucination as they start guessing answers. This happens when the models do not have real-time data and they predict answers based on patterns. Even major companies like Meta and OpenAI spend months thoroughly training their models; but by the time those models are released, they’re already starting to fall behind.
That is why strategies like RAG and fine-tuning are important. They keep the AI model relevant and accurate based on the real-time data essential for your business.
What is retrieval-augmented generation (RAG)?
Meta introduced RAG as a solution in 2020. It connects your LLM to current private company data such as internal documents, knowledge bases or support tickets. Following that, the model pulls relevant and current information in real-time instead of relying on initial training.
Usually when the training is over, traditional LLMs cannot update themselves or access a new set of information. RAG changes this scenario by searching through the connected database for accurate information.
How does RAG flow work?
There are four stages through which RAG models provide an answer:
- Stage 1: A user submits a question and this starts the RAG system.
- Stage 2: Complex algorithms work and search through the company’s internal database to find information for the question.
- Stage 3: The RAG model picks relevant information it finds and combines it with the question posed.
Note: Even at this stage, the AI model has not yet started thinking. It is just collecting the right information.
- Stage 4: AI combines the gathered information and what it already knows from the training and provides an accurate answer.
RAG uses semantic search to understand the meaning behind the question. It helps get past basic keyword search related to the question and provides some relevant data. For the data retrieval process to work, data engineers must set up a system with a proper combination of information storage systems and pipelines to connect an LLM to that data.
Benefits and challenges of RAG
| benefits | Challenges |
|---|---|
| Provides up-to-date and relevant information | Teams need to constantly maintain the database for accuracy |
| Minimizes hallucination and reduces fake content | Must manage consistent access to millions of documents |
| Ensures better security and privacy for companies | Bad chunking process can give misleading responses |
| You can track the answers back to the sources | Teams must engineer trusted environments for the process to work. |
What is fine-tuning?
On the other hand, fine-tuning goes a step further by training a general LLM like GPT-3 or GPT-4 to your company’s specific data. While the original model learns from various texts during initial training, fine-tuning allows the model to go through selected examples and teach it particular tasks. The model then produces specific answers for a domain, for instance fine-tuning a model on legal or medical language. This helps the model really understand your domain, industry terminology and use cases.
Workflow of fine-tuning
The fine-tuning process begins by exposing a pre-learned model like GPT 3 or 4 to a specific set of data examples. Engineers supervise and clean the database as per the domain’s need before feeding it to the model. They update the internal parameters regularly so the model can understand the language, tone and context specific to the business. Proper data preparation for AI is crucial for reliable fine-tuning results.
There are two ways to fine-tune a model: full fine-tuning and parameter-efficient fine-tuning (PEFT). Full fine-tuning updates all the model’s parameters, but for that you need a lot of computational power and memory. PEFT, on the other hand, updates only a small portion of the model. It is much faster and easier to run on simple hardware.
Benefits and challenge of fine-tuning
| Benefits | Challenges |
|---|---|
| Domain specific training and responses | You need high-quality credible data for the model |
| Highly efficient through model optimization | There is a risk of model becoming much too specific |
| Allows you to customize the tone and style of the model | It is very daunting to update the database of a fine-tuned model when there is a new update in the training data |
| You can fine-tune the model as per company’s needs | There may be a lack of sources to cite for facts or data to create transparency |
Difference between RAG and fine-tuning
There are some distinctive differences between RAG and fine-tuning.
- At the core, the biggest difference lies in how each accesses knowledge. RAG retrieves information on the fly from external sources like a document database or knowledge base. In contrast, fine-tuning bakes that knowledge directly into the model during the training process.
- RAG is easier to modify and generally faster to implement as long as your data is organized and searchable. Fine-tuning is more rigid.
- RAG needs a full ecosystem to function well: a vector database, a semantic search engine and a system to manage document ingestion. Fine-tuning leans heavily on machine learning capacity.
- Cost-wise, they also diverge. RAG is generally more cost-effective in terms of training, but maintaining the infrastructure can add to the overall cost. Fine-tuning is expensive upfront, especially if you are doing full fine-tuning.
- Fine-tuning offers better control over the model’s behavior. You can tune it to sound exactly the way you want. RAG depends on the source documents, so tone and style may vary unless your prompt documents are consistent.
- RAG is good for dynamic scenarios like customer support, FAQs, etc. Fine-tuning, however, shines in domain-specific tasks like generating legal contracts, medical content, etc.
Both approaches are powerful in their own right. Your decision on approach will depend on what kind of problem you are solving; how dynamic your knowledge base is and how much control you need over responses.
When to use RAG, fine-tuning or both?
The decision to pick the right strategy lies in your business needs, technical resources and the nature of your data.
If your application demands up-to-date information, RAG is often a better choice. It is easy to maintain and scale, especially when your knowledge base changes quickly. On the other hand, if you need consistent outputs, domain specific tone or want your model to perform well on repetitive tasks, fine-tuning is optimal.
In some cases, using both together delivers the best of both worlds. Fine-tune the base model and domain expertise and use RAG to fetch fresh or long-tail information. This hybrid approach can provide consistency while keeping the responses updated.
Ultimately, the choice depends on what matters more: freshness and flexibility (RAG), control and coherence (fine-tuning) or a balanced hybrid of both.
How do you make the right choice?
Making the right choice depends on several factors such as your budget, how often your domain changes, data privacy needs, available technical resources and level of control you require. Once you clearly understand your priorities, you will be able to choose the approach that best supports your business goals. For business leaders, boosting AI literacy can help your organization make better, long-term strategic decisions.
FAQs
Q.1 Is RAG better than fine-tuning?
Ans. Not always. RAG is better when your knowledge base changes frequently and you want to avoid retraining the model. Fine-tuning is better when you need consistent, domain-specific outputs. Think of RAG as flexible and up-to-date, while fine-tuning is precise and tailored.
Q.2 Can you use RAG and fine-tuning together?
Ans. Yes. Many enterprises combine both to get the best of both worlds. Fine-tune the model on core knowledge and tone, then use RAG to pull in fresh or long-tail information that wasn’t part of the training set.
Q.3 How expensive is fine-tuning compared to RAG?
Ans. Fine-tuning, especially full fine-tuning, is more expensive upfront because it involves retraining the model. RAG is cheaper to get started but can have ongoing infrastructure costs for maintaining your retrieval pipeline.
Q.4 What are examples of RAG in real-world applications?
Ans. Customer support bots that pull from internal documentation, AI assistants that reference real-time support tickets, and enterprise search systems that summarize internal PDFs or FAQs.
Q.5 Do I need in-house ML engineers for fine-tuning?
Ans. Yes, ideally. Fine-tuning requires experience in preparing training data, selecting model architecture, monitoring performance, and retraining. If your team doesn’t have that expertise, consider hiring a machine learning expert that can simplify it.
Bob Taylor
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