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Bigger isn’t always better: Examining the business case for multi-million token LLMs
Are we unlocking new frontiers in AI reasoning, or simply stretching the limits of token memory without meaningful improvements?
As enterprises weigh the costs of scaling infrastructure against potential gains in productivity and accuracy, the question remains: Are we unlocking new frontiers in AI reasoning, or simply stretching the limits of token memory without meaningful improvements? As companies adopt AI for complex tasks, they face a key decision: Use massive prompts with large context windows, or rely on RAG to fetch relevant information dynamically. Emerging innovations like GraphRAG can further enhance these adaptive systems by integrating knowledge graphs with traditional vector retrieval methods that better capture complex relationships, improving nuanced reasoning and answer precision by up to 35% compared to vector-only approaches.
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