RAG Glossary: Essential Retrieval Augmented Generation Terms
This comprehensive glossary defines key terms related to RAG technology. Understanding these concepts is essential for effectively implementing and optimizing Retrieval Augmented Generation systems.
Retrieval Augmented Generation (RAG)
A hybrid AI architecture that combines information retrieval systems with generative AI models. RAG enhances LLMs by retrieving relevant information from external knowledge sources to provide as context for generating responses.
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Knowledge Base
A structured or unstructured collection of information (documents, data, etc.) that serves as the source of information for the retrieval component in a RAG system.
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Embeddings
Numerical vector representations of text that capture semantic meaning. In RAG systems, embeddings are used to represent both queries and documents in a shared vector space to facilitate similarity matching.
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Vector Database
A specialized database optimized for storing and querying vector embeddings. These databases enable efficient similarity search, which is essential for the retrieval component of RAG systems.
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Chunking
The process of breaking down long documents into smaller, more manageable pieces (chunks) for embedding and retrieval in a RAG system. Effective chunking strategies balance context preservation with retrieval precision.
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Hallucination
When an AI model generates information that is factually incorrect or not supported by reliable sources. RAG systems aim to reduce hallucinations by grounding generated responses in retrieved factual information.
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Semantic Search
A search methodology that focuses on understanding the intent and contextual meaning of a query rather than just matching keywords. RAG systems typically use semantic search for the retrieval component.
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Context Window
The maximum amount of text that an LLM can process at once. In RAG systems, retrieved information must fit within the context window along with the query and any instructions.
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Embedding Model
A neural network model trained to convert text into vector embeddings that capture semantic relationships. Common embedding models used in RAG systems include models from OpenAI, Cohere, and open-source alternatives.
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Vector Similarity
A measure of how close two vectors are in embedding space, typically calculated using metrics like cosine similarity, Euclidean distance, or dot product. Used to identify relevant documents during retrieval.
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Inner State
Our proprietary methodology that enhances traditional RAG systems by maintaining a rich internal representation of conversational context and semantic relationships for more coherent and contextually aware AI responses.
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Multi-stage Retrieval
A RAG approach that employs multiple sequential retrieval steps, often using different techniques or granularities, to progressively refine the information provided to the LLM.
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