Retrieval-Augmented Generation (RAG)
An AI architecture that enhances language model outputs by first retrieving relevant documents from an external knowledge base, then using them as context for generation — reducing hallucinations and enabling up-to-date responses.
RAG has become the dominant enterprise pattern for deploying LLMs with private or current data. Rather than fine-tuning a model on proprietary data (expensive and inflexible), RAG retrieves relevant documents at query time and includes them in the prompt. This approach can reduce hallucination rates by 30-50%. The vector database market, which underpins RAG systems, is projected to reach $4.3 billion by 2028. Major platforms like LangChain, LlamaIndex, and enterprise solutions from AWS and Azure all prioritize RAG workflows.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Enterprise AI Adoption
The rate at which businesses integrate AI technologies into their operations, measured across functions like customer service, software development, marketing, and supply chain management.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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