RAG vs. fine-tuning

In the realm of generative AI, various techniques enhance the capabilities and performance of models in specific applications. Two such techniques are Retrieval Augmented Generation (RAG) and fine-tuning Foundation Models. While both methods improve the output of large language models (LLMs), they differ significantly in their approach and use cases.

Retrieval Augmented Generation (RAG)

RAG is a method that enhances a generative model’s responses by integrating external information retrieval into the generation process. This technique is particularly useful in scenarios where the model needs to provide accurate and up-to-date information that is not contained within its original training data.

How RAG works

RAG operates by combining the capabilities of a traditional language model with a retrieval system. The process involves the following steps:

Query processing: When a query is received, RAG first processes the query to understand its context and intent.
Information retrieval: It then uses this processed query to retrieve relevant information from an external database or knowledge base.
Response generation: The retrieved information is fed back into the language model, which synthesizes the external data with its pre-trained knowledge to generate a coherent and contextually enriched response.

Use cases for RAG

Question-answering systems: Enhance accuracy by providing the most current data.
Content creation: Generate detailed and informed content that requires external citations or up-to-date statistics.

Fine-tuning Foundation Models

Fine-tuning involves adjusting a pre-trained foundation model on a specific dataset to adapt its responses more closely to the needs of a particular application or domain. This method is critical for tailoring generalist models to perform well on specialized tasks.

How fine-tuning works

Fine-tuning adjusts the weights of a pre-trained model through continued training on a new, often smaller, dataset that contains examples more representative of the target task. The steps include:

Dataset preparation: Compile a dataset that reflects the specific nuances and requirements of the target domain.
Model adjustment: Train the model on this new dataset, allowing it to learn from these new examples and adjust its parameters accordingly.
Evaluation and iteration: Regularly evaluate the model’s performance on validation data, and iterate on the training process to optimize accuracy and relevance.

Use Cases for fine-tuning

Domain-specific applications: Adapt models to specialized fields like legal, medical, or technical domains.
Personalization: Customize responses based on user data or preferences.

Key differences

Feature	RAG	Fine-tuning
Primary goal	To enhance responses with external data.	To adapt model behavior to specific domains.
Data dependency	Depends on external databases for real-time data.	Relies on a specific training set relevant to the task.
Model adaptability	Combines retrieval with generation; adaptable to various data sources.	Tailors the model to specific content or user data.
Implementation complexity	Involves integrating retrieval mechanisms like vector databases with generative models.	Primarily requires training infrastructure like Domino Hardware Tiers and Jobs for model adaptation, whose progress can be tracked via Domino’s Experiment Management.

Feature

RAG

Fine-tuning

Primary goal

To enhance responses with external data.

To adapt model behavior to specific domains.

Data dependency

Depends on external databases for real-time data.

Relies on a specific training set relevant to the task.

Model adaptability

Combines retrieval with generation; adaptable to various data sources.

Tailors the model to specific content or user data.

Implementation complexity

Involves integrating retrieval mechanisms like vector databases with generative models.

Primarily requires training infrastructure like Domino Hardware Tiers and Jobs for model adaptation, whose progress can be tracked via Domino’s Experiment Management.

Next steps

Learn more about Retrieval Augmented Generation (RAG).