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.
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:
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Query processing: When a query is received, RAG first processes the query to understand its context and intent.
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Information retrieval: It then uses this processed query to retrieve relevant information from an external database or knowledge base.
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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.
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:
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Dataset preparation: Compile a dataset that reflects the specific nuances and requirements of the target domain.
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Model adjustment: Train the model on this new dataset, allowing it to learn from these new examples and adjust its parameters accordingly.
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Evaluation and iteration: Regularly evaluate the model’s performance on validation data, and iterate on the training process to optimize accuracy and relevance.
Feature | RAG | Fine-tuning |
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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. |
Learn more about Retrieval Augmented Generation (RAG).