Differences Between Retrieval-Augmented Generation (RAG) and Various Fine-Tuning Methods

Surahutomo Aziz Pradana
6 min readMay 25, 2024

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Hello guys!

As we know when developing AI models, particularly for natural language processing (NLP) tasks, several approaches can actually employed to enhance the model’s performance and accuracy.

These approaches include Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Parameter-Efficient Fine-Tuning (PEFT).

Now, let’s understand the differences between these methods since it is crucial for selecting the best strategy for a given our application!

Overview of Fine-Tuning Methods

Traditional Fine-Tuning is a way of adapting a pre-trained model to a specific task or dataset by further training it on new data, which customizes the model’s parameters to improve performance on the target task.

Low-Rank Adaptation (LoRA) is a fine-tuning method that adds low-rank matrices to the layers of a pre-trained model, which are then trained on task-specific data. This method reduces the number of trainable parameters, making fine-tuning more efficient.

Quantized LoRA (QLoRA) is avariant of LoRA that combines low-rank adaptation with quantization techniques to further reduce the model size and computational requirements without significantly sacrificing performance.

Parameter-Efficient Fine-Tuning (PEFT) is a way to encompasses various techniques, including LoRA, that aim to fine-tune a model by modifying a small number of parameters, thereby reducing the computational cost and resource requirements.

Fine-tuning in generative AI refers to the process of taking a pre-trained model and further training it on a more specific dataset or for a more specific task. This technique is widely used to adapt large-scale pre-trained models to specialized tasks with better performance and efficiency.

Now let’s understand the steps!

Steps in Fine-Tuning

  1. Pre-trained Model Selection: Start with a generative model that has been pre-trained on a large and diverse dataset. Examples include GPT, BERT, or image generators like GANs and VAEs.
  2. Dataset Preparation: Prepare a dataset that is relevant to the specific task you want the model to perform. This dataset should be annotated and cleaned to ensure quality.
  3. Adjusting the Model: Configure the model to be fine-tuned. This may involve adjusting the architecture slightly, setting up a new output layer, or defining specific loss functions relevant to the task.
  4. Training: Train the model on the prepared dataset. This involves feeding the task-specific data into the model and updating its weights through backpropagation. The training can be full or partial, depending on the requirement. Often, the earlier layers of the model are frozen (i.e., their weights are not updated) to retain general knowledge, while the later layers are fine-tuned.
  5. Evaluation and Iteration: After training, evaluate the model’s performance on a validation set. Based on the results, you might need to iterate on the dataset, training parameters, or model architecture to achieve optimal performance.

Benefits of Fine-Tuning

  • Improved Performance: Fine-tuning allows the model to adapt to specific nuances of the target dataset, resulting in better performance for the given task compared to a general-purpose model.
  • Efficiency: It is more computationally efficient than training a model from scratch since it leverages the knowledge already embedded in the pre-trained model.
  • Flexibility: Fine-tuning can be applied to various domains and tasks, such as text generation, translation, image synthesis, and more.
  • Reduced Data Requirement: Fine-tuning typically requires less data than training a model from scratch because the pre-trained model already captures a broad understanding of language or visual patterns.

How about RAG ?

Retrieval-Augmented Generation (RAG)

RAG enhances generative AI models by incorporating external information through a retrieval process. Instead of relying solely on the pre-trained model’s knowledge, RAG dynamically retrieves relevant documents or data from an external source and integrates this information into the generative process.

Key Differences Between RAG and Fine-Tuning Methods

  • Fine-Tuning (Traditional, LoRA, QLoRA, PEFT): Adjusts the model’s internal parameters based on the training data. The model learns to generalize from the data it was fine-tuned on, embedding the knowledge directly into its weights.
  • RAG: Uses an external retrieval mechanism to fetch relevant information at inference time. The generative model uses this retrieved information to produce contextually accurate responses.

Data Requirements

  • Fine-Tuning (Traditional, LoRA, QLoRA, PEFT): Requires a substantial amount of task-specific data to effectively adjust the model’s parameters. The more diverse and comprehensive the fine-tuning dataset, the better the model performs on the specific task.
  • RAG: Relies on a well-curated external knowledge base or database for retrieval. The model can handle tasks with less fine-tuning data by leveraging the external information it retrieves.

Flexibility and Adaptability

  • Fine-Tuning (Traditional, LoRA, QLoRA, PEFT): Once fine-tuned, the model’s knowledge is static and limited to the information embedded during the training process. Updating the model’s knowledge requires re-fine-tuning with new data.
  • RAG: Offers dynamic adaptability as it retrieves up-to-date information from an external source. This allows the model to provide current and relevant responses without needing to re-train.

Resource Utilization

  • Traditional Fine-Tuning: Can be computationally intensive, especially for large models, as it involves adjusting many parameters and requires significant computational resources for training.
  • LoRA and QLoRA: Reduce the computational load by introducing low-rank matrices and quantization, respectively. These methods make fine-tuning more efficient and less resource-intensive.
  • PEFT: Focuses on modifying a small number of parameters, thus reducing the overall computational cost.
  • RAG: Shifts some computational load to the retrieval process. While retrieval can also be resource-intensive, it allows the generative model to remain relatively lightweight since it doesn’t need to internalize all knowledge.

Response Accuracy and Relevance

  • Fine-Tuning (Traditional, LoRA, QLoRA, PEFT): The accuracy and relevance of responses depend on how well the model was fine-tuned on the specific task data. It may struggle with queries requiring information not covered during training.
  • RAG: Enhances response accuracy and relevance by incorporating real-time retrieved information, making it more capable of handling diverse and specific queries effectively.

Use Cases

  • Fine-Tuning (Traditional, LoRA, QLoRA, PEFT): Suitable for tasks where the domain-specific data is stable and well-defined, such as sentiment analysis, language translation, and specific classification tasks.
  • RAG: Ideal for applications requiring up-to-date information and diverse knowledge, such as customer support systems, interactive chatbots, and any scenario where the AI needs to draw on a wide range of external data.

Now we understand, but what scenario we can benefit this ?

Example Scenarios

Customer Support System:

  • Traditional Fine-Tuning: The model is fine-tuned on historical customer interactions and FAQs. It provides responses based on the patterns learned during training.
  • LoRA/QLoRA/PEFT: The model is efficiently fine-tuned on the same data, but with fewer resources. It delivers similar responses as traditional fine-tuning but more cost-effectively.
  • RAG: The model retrieves the latest product manuals, recent customer inquiries, and other dynamic content to provide up-to-date and contextually rich responses.

Content Generation:

  • Traditional Fine-Tuning: A generative model fine-tuned on a specific corpus (e.g., a collection of news articles) generates content based on the themes and styles in that corpus.
  • LoRA/QLoRA/PEFT: The same generative model is fine-tuned more efficiently, achieving similar performance with lower resource usage.
  • RAG: The model retrieves recent articles and current events information, integrating this data to generate content that is both timely and relevant.

Summary

Retrieval-Augmented Generation (RAG) and various fine-tuning methods, including LoRA, QLoRA, and PEFT, offer distinct advantages and cater to different needs. Fine-tuning methods embed knowledge directly into the model, making them suitable for stable, well-defined tasks. Techniques like LoRA, QLoRA, and PEFT enhance fine-tuning efficiency, reducing computational costs. In contrast, RAG dynamically incorporates external information, providing flexibility and the ability to handle a wide range of queries with current and relevant responses.

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Surahutomo Aziz Pradana

Google Developer Expert - Firebase, Co-Lead GDG Jakarta, GDSC Lead PENS, Engineering Manager, AR/VR Tech Lead, Fullstack Engineer