Long Context

Long Context refers to LLMs that process very large input prompts — typically 100K tokens or more, up to multi-million-token models. Context windows have expanded dramatically: from 2K-4K in original GPT-3, to 32K in GPT-4 (March 2023), to 128K in GPT-4 Turbo and Claude 2.1 (late 2023), to 200K in Claude 3, to 1-2 million in Gemini 1.5 Pro (2024-2025). Long context enables entirely new use cases: ingesting entire books, complete codebases, long video transcripts, or thousands of documents in a single prompt. The technical challenges include memory consumption (attention scales quadratically with context length without optimization), inference latency (longer prompts take longer), and quality degradation (lost-in-the-middle issues at long contexts). Architectural innovations enabling long context include grouped-query attention (GQA), sliding-window attention, mixture-of-experts routing, and various memory-efficient attention implementations. AI governance, AI compliance, and AI risk management programs include long-context capability in model selection supporting responsible AI through whole-document workflows in enterprise AI deployments worldwide.

Centralpoint Routes Long-Context Workloads Intelligently: Oxcyon's Centralpoint AI Governance Platform sends very long inputs to Gemini, Claude, and GPT-4 long-context models — alongside Llama and embedded options. Centralpoint meters every token, keeps prompts and skills on-prem, and embeds long-context chatbots into your portals via a single JavaScript line.


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