Retrieval Augmented Generation
AI ArchitectureWhat It Means
RAG is like giving your AI assistant access to a company library before it answers questions. Instead of relying only on its training data, the AI first searches through your current documents, databases, and knowledge systems to find relevant information, then uses that fresh data to generate more accurate and up-to-date responses.
Why Chief AI Officers Care
RAG solves the critical problem of AI hallucination and outdated information by grounding responses in your actual business data, making AI outputs more trustworthy and actionable. It also provides better governance since you can control exactly which knowledge sources the AI accesses, ensuring compliance and data security while keeping AI responses current without expensive model retraining.
Real-World Example
A customer service AI using RAG would first search your current product manuals, recent policy updates, and customer history before answering a support question, ensuring it provides accurate information about your latest features and the customer's specific situation rather than generic or outdated responses.
Common Confusion
Many executives think RAG requires replacing their existing AI systems entirely, when it's actually an enhancement that can be added to current language models. Others assume it's just a fancy search engine, missing that it intelligently combines retrieved information with AI generation for contextual, conversational responses.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, RAG enables AI systems to access current medical literature, patient records, and clinical guidelines to ...
Finance: In finance, RAG enables AI systems to access real-time market data, regulatory filings, and internal risk documents befo...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
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