What is Retrieval-Augmented Generation (RAG)?
Estimated reading time: 4 minutes
Retrieval-Augmented Generation (RAG) is a cutting-edge method in natural language processing (NLP) that combines retrieval-based and generation-based models. It is particularly effective for producing informative and contextually relevant text, with applications in question answering, dialogue systems, and content creation.
Learning Objectives
After reading this article, you will be able to:
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Understand how the RAG model works
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Explain how RAG addresses the limitations of generative AI
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Explore key applications and use cases of RAG
Overview of the RAG Model
RAG works in three stages:
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Retrieval: It searches a predefined corpus for documents relevant to the user query.
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Augmentation: Retrieved documents are used to enrich the input to the generative model.
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Generation: The generative model uses both the query and the retrieved information to generate a final response.
This hybrid process improves accuracy and contextuality, overcoming the shortcomings of pure generation models.
Limitations of Generative AI Models
Traditional generative models (e.g., GPT-3, GPT-4) face several challenges:
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May generate plausible-sounding but incorrect content (“hallucination”)
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Cannot access knowledge beyond their training cutoff
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Limited context window makes it hard to handle long conversations or documents
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May lack depth or precision on specific or technical queries
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High resource consumption for long-form generation
How RAG Addresses These Limitations
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Grounded in real data, reducing hallucination
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Up-to-date information, overcoming static knowledge limitations
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Extended context by leveraging external content
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More accurate and specific responses
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Efficient generation, thanks to focused retrieval
Key Components of RAG
Retrieval Component
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Role: Identifies relevant documents from large corpora
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Techniques: Uses BM25 or dense retrievers for high-relevance search
Generation Component
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Role: Produces coherent responses based on retrieved content
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Models: Includes models like GPT-3 or fine-tuned BERT
Benefits of RAG
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Improved factual accuracy
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Enhanced contextual relevance
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Flexible application across NLP tasks
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Ability to retrieve and use up-to-date data
Applications of RAG
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Question answering
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Content generation
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Customer support
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Enhanced search engines
Implementing RAG on Google Cloud
Google Cloud offers robust infrastructure for building RAG applications:
Vertex AI
A full suite for training and deploying LLMs with RAG support
BigQuery
Provides large-scale, efficient data retrieval for the RAG pipeline
Key Features on Google Cloud
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Scalability: Handles large-scale retrieval and generation
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Integration: Connects seamlessly with data sources and APIs
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Customization: Tailored to specific business needs
Example: History QA
For the question “What were the causes of World War II?”, the RAG system first retrieves relevant historical documents, then generates an accurate and detailed response.
Example Use Case: Customer Support
Integrating BigQuery with RAG allows customer support systems to access the latest policies, ensuring accurate and timely responses.
Summary
RAG enhances generative AI by grounding it in retrieved knowledge, addressing key issues like hallucination and outdated information. It is proving valuable in multiple domains including question answering, content creation, support services, and search augmentation, making it a powerful next step in AI evolution.