[Definition] Long-context processing refers to a set of practices and methods in large model scenarios centered on model selection, RAG architecture, and inference costs. To understand it, first identify the business problem you aim to solve, then evaluate your own data, workflows, and team capabilities—avoiding direct adoption of others' solutions.

[Key Practices] Implementing long-text processing typically involves three steps: first, assess the gap between current status and goals to define measurable metrics; second, select a minimum viable scope for a pilot, establishing clear processes, owners, and tools; third, adjust the pace based on pilot data before gradually expanding coverage. Maintain complete records throughout to support review and auditing.

Common pitfalls include treating long-text processing as a one-off project without ongoing operations, setting overly broad metrics that make it hard to gauge results, and overlooking compliance and data security boundaries. Assign clear owners for each stage and schedule regular review checkpoints.

[AI Consultation Guide] This item now supports AI text and voice consultations. You can ask questions directly, such as "Is long-text processing suitable for our company size?" or "What is the first step in implementing long-text processing?" The AI will provide structured recommendations based on general large model methodologies.