RAG vs. fine-tuning

Though generative AI has come a long way since its inception, the task of generating automated responses in real time to user queries is still a significant challenge. As enterprises race to incorporate gen AI into their processes to reduce costs, streamline workflows and stay ahead of competitors, they often struggle with getting their chatbots and other models to reliably generate accurate answers.

The difference between RAG and fine-tuning is that RAG augments a natural language processing (NLP) model by connecting it to an organization’s proprietary database, while fine-tuning optimizes deep learning models for domain-specific tasks. RAG and fine-tuning have the same intended outcome: enhancing a model’s performance to maximize value for the enterprise that uses it.

RAG uses an organization’s internal data to augment prompt engineering, while fine-tuning retrains a model on a focused set of external data to improve performance.

RAG plugs an LLM into stores of current, private data that would otherwise be inaccessible to it. RAG models can return more accurate answers with the added context of internal data than they otherwise would be able to without it.

A fine-tuned model typically outperforms its corresponding base model, such as GPT-3 or GPT-4, when applying its training with domain-specific data. The fine-tuned LLM has a better understanding of the specific domain and its terminology, allowing it to generate accurate responses.

Without continual access to new data, large language models stagnate. Modern LLMs are massive neural networks requiring huge data sets and computational resources to train. Even the largest LLM vendors, such as Meta, Microsoft and OpenAI, periodically retrain their models—which makes any LLM near-instantly obsolete the moment it’s released into the wild.

When models can’t learn from new data, they often hallucinate or confabulate: a phenomenon that occurs when gen AI models “make up” answers to questions they cannot definitively answer. Generative AI models use complex statistical algorithms to predict answers to user queries. If a user asks something the AI can’t easily find within its training data set, the best it can do is guess.

RAG is an LLM optimization method introduced by Meta AI in a 2020 paper called "Retrieval-Augmented Generation for Knowledge-Intensive Tasks".^[1] It is a data architecture framework that connects an LLM to an organization’s proprietary data, often stored in data lakehouses. These vast data platforms are dynamic and contain all the data moving through the organization across all touchpoints, both internal and external.

Retrieval augmented generation works by locating information in internal data sources that is relevant to the user’s query, then using that data to generate more accurate responses. A data "retrieval" mechanism is added to "augment" the LLM by helping it "generate" more relevant responses.

RAG models generate answers via a four-stage process:

1. Query: A user submits a query, which initializes the RAG system.

2. Information retrieval: Complex algorithms comb the organization’s knowledge bases in search of relevant information.

3. Integration: The retrieved data is combined with the user’s query and given to the RAG model to answer. Up to this point, the LLM has not processed the query.

4. Response: Blending the retrieved data with its own training and stored knowledge, the LLM generates a contextually accurate response.

When searching through internal documents, RAG systems use semantic search. Vector databases organize data by similarity, thus enabling searches by meaning, rather than by keyword. Semantic search techniques enable RAG algorithms to reach past keywords to the intent of a query and return the most relevant data.

RAG systems require extensive data architecture construction and maintenance. Data engineers must build the data pipelines needed to connect their organization’s data lakehouses with the LLM.

To conceptualize RAG, imagine a gen AI model as an amateur home cook. They know the basics of cooking, but lack the expert knowledge—an organization’s proprietary database—of a chef trained in a particular cuisine. RAG is like giving the home cook a cookbook for that cuisine. By combining their general knowledge of cooking with the recipes in the cookbook, the home cook can create their favorite cuisine-specific dishes with ease.

To use RAG effectively, data engineers must create data storage systems and pipelines that meet a series of important criteria.

To enhance RAG system functions and enable real-time data retrieval, the data must be meticulously organized and maintained. Up-to-data metadata and minimal data redundancy help ensure effective querying.

Dividing unstructured data, such as documents, into smaller pieces can facilitate more effective retrieval. “Chunking” the data in this way allows RAG systems to return more accurate data while reducing costs because only the most relevant portion of the document will be included in the prompt for the LLM.

Next, the chunks are embedded—a process that converts text into numbers—into a vector database.

Data pipelines must include security restrictions to prevent employees from accessing data beyond the scope of their respective roles. And in the wake of landmark privacy legislation such as the EU’s GDPR, organizations must apply rigorous data protections to all internal data. Personally identifiable information (PII) must never be made available to unauthorized users.

The RAG system combines the user’s query with the sourced data to create a tailored prompt for the LLM. A continual prompt-tuning process facilitated by other machine learning models can strengthen the RAG system’s question-answering ability over time.

Fine-tuning is the process of retraining a pretrained model on a smaller, more focused set of training data to give it domain-specific knowledge. The model then adjusts its parameters—the guidelines governing its behavior—and its embeddings to better fit the specific data set.

Fine-tuning works by exposing a model to a data set of labeled examples. The model improves on its initial training as it updates its model weights based on the new data. Fine-tuning is a supervised learning method, which means the data used in training is organized and labeled. By contrast, most base models undergo unsupervised learning, in which the data is unsorted—the model must categorize it on its own.

Again imagining a gen AI model as a home cook, fine-tuning would be a cooking course in a specific cuisine. Before taking the course, the home cook would have a general understanding of cooking basics. But after undergoing culinary training and acquiring domain-specific knowledge, they’d be much more proficient in cooking that type of food.

Models can be either fully fine-tuned, which updates all their parameters or fine-tuned in a way that updates only the most relevant parameters. This latter process is known as parameter-efficient fine-tuning (PEFT) and excels at making models more effective in a certain domain while keeping training costs low.

Fine-tuning a model is compute-intensive and requires multiple powerful GPUs running in tandem—let alone the memory to store the LLM itself. PEFT enables LLM users to retrain their models on simpler hardware setups while returning comparable performance upgrades in the model’s intended use case, such as customer support or sentiment analysis. Fine-tuning especially excels at helping models overcome bias, which is a gap between the model’s predictions and actual real-world outcomes.

Pretraining occurs at the very start of the training process. The model weights or parameters are randomly initialized, and the model commences training on its initial data set. Continuous pretraining introduces a trained model to a new unlabeled data set in a practice known as transfer learning. The pretrained model "transfers" what it has learned so far to new external information.

By contrast, fine-tuning uses labeled data to hone a model’s performance in a selected use case. Fine-tuning excels at honing a model’s expertise at specific tasks, while continuous pretraining can deepen a model’s domain expertise.