The final battle: RAG vs. Fine-tuning

As AI and data-driven solutions take center stage, the debate between retrieval-augmented generation (RAG) and fine-tuning LLMs remains key. In conversation with GOALParita Desai, Senior Solutions Architect at Fractal, provided insights into these approaches and explained their different roles, benefits and practical applications in different industries.

Both RAG and fine-tuning are complex processes that need to be carefully planned and executed. Although they serve different purposes, their workflows share some common elements, particularly in data preparation and delivery. Desai explained the steps required to produce a fine-tuned model and RAG system.

Bringing a finely tuned model into production

• Data preparation: The process begins with preparing labeled data that is specific to the task at hand. This labeled dataset forms the basis for training the model and ensures that it can generate accurate results.

• Model training: Once the data is ready, the pre-trained model is fine-tuned by training it on the labeled dataset. This step tailors the model’s capabilities to the specific requirements of the task, such as converting legacy SAS code to BigQuery, as Desai described in one of her projects.

• Model deployment: After fine-tuning, the model is deployed to the production environment and is ready for use in real applications.

• conclusion: At runtime, the fine-tuned model generates predictions or outputs based on the data it was trained on and provides domain-specific answers that meet the user’s requirements.

Production of a RAG system

• Data preparation: RAG begins by integrating a large corpus of internal and external data. This corpus serves as the basis from which the LLM can retrieve information in response to user queries.

• Retrieval component: A retrieval component, often implemented as a vector database, is then set up to search this large corpus and enable the system to efficiently find relevant information.

• LLM Integration: The next step involves integrating the retrieval component into the LLM. The LLM uses the output of the retrieval system in combination with the user query and prompt to generate a contextual response.

• Mission: Both the retrieval system and the language model are deployed in the production environment to ensure smooth operation.

• conclusion: During inference, the system retrieves information from the corpus and supplements the LLM with this data. The LLM then generates an evidence-based answer by combining the user query, the prompt, and additional information from the query system.

Practical use cases and integration

Desai presented several practical applications for RAG and fine-tuning. In the legal sector, Fractal implemented a legal advisor that used RAG to analyze client documents and provide financial advice. In healthcare, a similar approach was used to create an advisor agent, demonstrating the versatility of RAG.

One practical application of RAG that Desai mentioned was using a query database to provide specific customer data, enabling more precise answers. “By building a RAG pipeline and feeding context along with the user query, we get precise answers tailored to the customer’s data,” she explained.

Fine-tuning, on the other hand, is about getting domain-specific answers. Desai described a code conversion project for a banking client that involved converting old SAS codes to BigQuery. “We used fine-tuning by creating a labeled dataset of 500 points with input-output pairs and feeding it to the pre-trained LLM,” she said.

Desai emphasized the importance of chunking when processing large data sets, as demonstrated in a code conversion project for a banking client.

Best practices and recommendations

Comparing the benefits of RAG and fine-tuning, Desai found that RAG provides accuracy and dynamic responses by incorporating real-time context, while fine-tuning provides domain-specific responses by training the model on labeled data.

“RAG is less costly and requires fewer resources than fine-tuning, which requires more infrastructure and computing power,” she explained.

There are limitations, however. Desai pointed out that RAG may have problems with keywords that have multiple meanings in different contexts. “Combining semantic search with keyword search, called hybrid search, can mitigate this problem,” she suggested.

Desai advises companies to experiment first and evaluate the availability of data for the specific use case. Whether the goal is cost efficiency, dynamic or static data processing, or integration complexity, understanding the business problem is key to deciding between RAG and fine-tuning.

In summary, RAG and fine-tuning offer unique benefits and are not mutually exclusive. “It’s not a question of one being better than the other; it depends on the business problem you’re trying to solve,” Desai stressed. The choice between these approaches should be driven by the organization’s specific needs and goals.