Cutting RAG Inference Costs 6x Starts with Deciding What Never Reaches the LLM
Most teams building retrieval augmented generation (RAG) systems for high stakes classification make the same architectural bet: Route every ambiguous case.
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Inference
Inference is the process of using a trained AI model to make predictions or generate text based on new inputs. During inference, data flows forward through the neural network to produce an output, without modifying the model's weights.
RAG
Retrieval-Augmented Generation (RAG) is a methodology that optimizes the output of a Large Language Model (LLM) by referencing an authoritative, external knowledge base or Vector Database before generating a response. RAG helps models access real-time information and drastically reduces hallucination.
LLM
A Large Language Model (LLM) is a type of artificial intelligence model trained on vast amounts of text data to understand, generate, and manipulate natural language. Built on the Transformer architecture, LLMs use billions of parameters to recognize semantic patterns and reasoning relationships.
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