Confidence Signaling - Continuous Learning Layer

Part one. Confidence signaling. For every interaction, PromptQL calculates a confidence score, approaching it almost like a human analyst would. When it encounters a question, it first checks its operational context, asking itself, Do I actually know how to reason accurately about this question? That context lives inside a living operational semantic lay, one that captures your company's unique language, its logic, its definitions, and if there's ambiguity or missing context, say two columns were both labelled customer ID or a KPI wasn't clearly defined, PromptQL doesn't bluff. Just like a smart human analyst, it makes its assumptions explicit. If you asked a teammate, they would probably check the docs, ask around. If something was not clear, they would make a thoughtful assumption or ask for clarification. PromptQL does the same. It tells you what it knows, what it's unsure about, and what it's assuming. And that's how it builds trust through transparency that feels genuinely human. In part two, we'll explore how users can steer and teach the system in real time.