Log

Source: https://www.motivenotes.ai/p/what-makes-5-of-ai-agents-actually Selected points: --- Most founders think they’re building AI products. They’re actually building context selection systems. 'The base models are the soil; context is the seed.' Context engineering considered as LLM-native feature engineering: - Selective context pruning = feature selection - Context validation = schema/type/recency checks - 'Context observability' = trace which inputs improved/worsened output quality - Embedding augmentation with metadata = typed features + conditions Goal: Treat context like a versioned, auditable, testable artifact, not a string blob. Important: - You must trace which inputs led to which outputs (lineage) - You must respect row level, role based access (policy gating) - If two employees ask the same question, but have different permissions, the model output should differ. AI chat conversation works when it removes a learning curve.