RAG vs Fine tuning

Today, we are going to learn the difference between RAG and fine-tuning in the exciting field of Generative AI.

In Generative Artificial Intelligence (GenAI), we often hear terms like LLMs, vector databases, RAG, and fine-tuning. Before diving into RAG vs fine tuning, let’s see what each of these terms means in GenAI.

Key concepts

LLMs: Large language models or LLMs are AI models that can understand and output natural language text, for example, Gemini, and GPT-4.

Vector Databases: It is a database of vector embeddings stored as indexes for faster retrieval and similarity search like pinecone and qdrant.

RAG: Retrieval Augmented Generation or RAG is an information retrieval process. It utilizes LLM and vector databases to output the most relevant answer based on a given set of documents.

Finetuning: Finetuning is a general concept of Machine learning which means altering the weights and biases of a pre-trained ML model on a new dataset so the ML model can efficiently predict as per the new dataset.

I believe the difference must be clear from the definitions provided above. But, if you want to dig deeper into RAG vs fine tuning, keep reading.

Understanding RAG (Retrieval-Augmented Generation)

What is RAG?

RAG (Retrieval-Augmented Generation) is a technique used in natural language processing (NLP) that combines retrieval-based methods with generative models to improve the quality and relevance of generated responses.

How does RAG work?

Here’s how it works:

1. Retrieval: In RAG, an external knowledge base (such as a database of documents or a set of text passages) is searched to retrieve relevant information or context based on an input query. This step ensures that the generated response is grounded in actual data or factual content.
2. Augmentation: The retrieved information is then fed into a generative model, such as a Transformer-based language model (e.g., GPT), which uses this information as context to generate a more informed and relevant response.
3. Generation: The generative model produces the final output, typically a text response, that is enhanced by the contextual information provided by the retrieval step.

Here’s an example of how Retrieval-Augmented Generation (RAG) works in practice:

Scenario: Question Answering about a Historical Event

User Input (Query):
“Who was the first person to walk on the moon, and what did they say when they landed?”

RAG Process

Retrieval:

• The system searches a large knowledge base or document collection (such as Wikipedia or a database of NASA transcripts) to find relevant passages or documents that contain information about the first moon landing.
• Retrieved Passage 1: “Neil Armstrong was the first person to walk on the moon on July 20, 1969.”
• Retrieved Passage 2: “When he stepped onto the moon, Armstrong said, ‘That’s one small step for man, one giant leap for mankind.'”

Augmentation:

• The retrieved information is fed into a generative model. The model now has context and specific details to use in generating a response.

Generation:

• The generative model produces a response based on both the query and the retrieved information.

Final Output:
“Neil Armstrong was the first person to walk on the moon on July 20, 1969. When he took his first step onto the lunar surface, he famously said, ‘That’s one small step for man, one giant leap for mankind.'”

Explanation

As you can see from this example, all of the steps in RAG prove to be beneficial in generating a relevant and accurate response. For example, if we leave the retrieval step out of RAG, the LLM will not answer in great detail and the answer will sound less accurate. All of the steps combined increase the probability of an efficient response from the ML Model.

Advantages of RAG

• Ability to handle large-scale data
• flexibility in dealing with dynamic information
• lower computational cost compared to training large language models from scratch

Understanding Fine-Tuning

What is fine-tuning?

Fine-tuning is a process in machine learning where a pre-trained model is further trained on a new dataset to adapt it to a specific task or domain.

This approach leverages the knowledge the model has already learned from a large, general-purpose dataset and refines it to make it more applicable to a new, often more specific, dataset.

Here’s a breakdown of how fine-tuning works:

How does Finetuning work?

1. Start with a Pre-trained Model

• Pre-trained Model: This model has already been trained on a large dataset, like ImageNet for image classification or a vast text corpus for language models. The model has learned general features or patterns, such as edges and textures in images or syntax and grammar in text.
• Base Model Architecture: For example, in NLP, you might start with a model like BERT or GPT, which has learned to predict masked words or generate text.

2. Prepare the New Dataset

• Task-specific Data: Collect and preprocess the dataset relevant to your specific task. This dataset is usually smaller and more specialized than the dataset used to train the original model.
• Data Preprocessing: Ensure the new dataset is in a format that the model can use, matching the input structure the model expects.

3. Modify the Model (Optional)

• Adjust the Model Architecture: In some cases, you might modify the pre-trained model, such as changing the output layer to match the number of classes in a classification task or adjusting certain layers to focus on different features.

4. Fine-tuning the Model

• Unfreeze Layers: Decide which layers of the pre-trained model will be trained further. Often, the earlier layers (which capture very general features) are kept frozen, and only the later layers are unfrozen for training, as they are more task-specific.
• Set Learning Rate: Use a lower learning rate than usual. Since the model already has learned useful features, a lower learning rate helps make small adjustments without drastically altering the pre-trained weights.
• Training: Train the model on the new dataset. The model’s weights are updated based on the new data, allowing it to specialize in the new task. The fine-tuning process typically requires fewer epochs than training a model from scratch since the model is already well-initialized.

5. Evaluate and Test

• Validation: Continuously evaluate the model’s performance on a validation set during fine-tuning to avoid overfitting.
• Testing: Once fine-tuning is complete, test the model on a separate test set to ensure it generalizes well to new data.

Example

Imagine you have a pre-trained BERT model that has been trained on general text data. You now want to fine-tune it for sentiment analysis on a dataset of product reviews.

1. Pre-trained BERT Model: Already understands general language patterns.
2. New Dataset: A collection of labeled product reviews (positive or negative sentiment).
3. Fine-tuning: Train the BERT model on this dataset, adjusting its weights to better understand and classify the sentiment in the product reviews.
4. Result: The fine-tuned BERT model becomes highly effective at predicting the sentiment of new product reviews.

In summary, fine-tuning allows you to adapt a powerful pre-trained model to a specific task with relatively little additional training, leveraging the general knowledge the model has already acquired.

RAG vs Fine Tuning – Key Differences

Aspect	RAG	Finetuning
Purpose	Enhances generation with external, relevant information.	Adapts a pre-trained model to a specific task with new data.
Data Handling	Uses external knowledge base for retrieval.	Uses a task-specific dataset to update model weights.
Model Adaptation	Combines retrieval and generation without significant internal changes.	Updates model weights based on new data to specialize the model.
Use Cases	Question answering, dialogue systems needing factual grounding.	Sentiment analysis, and text classification for specific tasks.
Training & Inference	Sentiment analysis, and text classification for specific tasks.	Trains on task-specific data and performs inference based on the updated model.

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Scenarios for Using RAG

1. Question Answering Systems: Use when the system needs to provide accurate answers based on a large, external knowledge base or documents
2. Dialogue Systems: Use when generating responses that require grounding in specific, detailed, or up-to-date information.
3. Summarization: Use when summarizing content from diverse sources or large documents to ensure the summary is based on the latest or most relevant information.
4. Knowledge-Based Systems: Use when interacting with users about specific domains where up-to-date or extensive external knowledge is crucial.
5. Fact-Checking: Use when verifying facts or claims against an external source to provide accurate and reliable information.

Scenarios for Using Fine-Tuning

1. Sentiment Analysis: Use when analyzing sentiment in a specific type of text, such as product reviews, where the model needs to be specialized to detect sentiment nuances in that domain.
2. Text Classification: Use when classifying text into categories (e.g., spam detection, topic categorization) based on a dataset specific to the classification task.
3. Named Entity Recognition (NER): Use when identifying entities (names, locations, organizations) in a domain-specific context, such as medical or legal texts.
4. Language Translation: Use when adapting a translation model to perform well on specific language pairs or domain-specific language.
5. Customer Support: Use when fine-tuning a model to handle domain-specific customer queries, making it more effective in understanding and responding to customer support tickets.

These scenarios help determine whether RAG or Fine-Tuning is more appropriate based on whether you need dynamic retrieval of external information or a model adapted or finetuned to a specific task or dataset.

Combining RAG and Fine-Tuning

Hybrid Approaches:

• Complementing Each Other: RAG can enhance a model’s responses with external, real-time information while fine-tuning adapts the model’s understanding to specific tasks or domains. Combining both allows for accurate, contextually relevant responses tailored to a specific domain.
• Examples: Systems like advanced question-answering frameworks or specialized customer support bots use fine-tuning for domain expertise and RAG for up-to-date information retrieval.

Best Practices for Integration:

Tips:

• Fine-tune the model first to specialize it for the specific task or domain.
• Implement RAG to augment the model’s responses with external information during inference.

Considerations:

• Ensure efficient retrieval by optimizing the knowledge base.
• Balance the trade-off between retrieval quality and fine-tuning specificity to maintain high performance and relevance.

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Summary: RAG vs Fine Tuning

The article “RAG vs Fine Tuning” compares two approaches for enhancing machine learning models: Retrieval-Augmented Generation (RAG) and Fine-Tuning.

RAG integrates retrieval mechanisms with generative models to provide accurate and contextually relevant responses by dynamically pulling in information from external sources. It is effective for tasks requiring up-to-date or detailed knowledge, such as question answering and summarization.

Fine Tuning, on the other hand, involves adapting a pre-trained model to a specific task or dataset by updating its weights with additional task-specific data. This method is useful for tasks like sentiment analysis and text classification where the model needs to be specialized to perform well in a particular domain.

Hybrid Approaches:
Combining RAG and fine-tuning leverages the strengths of both methods. Fine-tuning can specialize the model for a task, and RAG can augment responses with external information. Best practices for this integration include starting with fine-tuning and using RAG to enhance responses while balancing retrieval quality and task specialization.

This comparison highlights how each method can be applied individually or together to achieve optimal performance in various machine learning tasks.