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Retrospective Verification reveals the criteria, assumptions and conditions behind AI claims so they can be evaluated and checked more precisely.

Inter-Response Consistency compares multiple AI outputs to identify stable concepts, meaningful variations and claims that need independent verification.

https://docs.google.com/document/d/19ng87A9jWbUwDk8FwrjDESGP-DIJFLM7MNEopJZfIEU/edit?tab=t.0#heading=h.avasq24k0y3e

AI Response Analysis evaluates claims, sources and evidence to determine how much trust a generative AI output deserves.

Prompt Structuring organizes context, instructions and constraints to reduce ambiguity and make generative AI outputs more targeted and controllable.

The Prompt Model organizes the key dimensions of a prompt to make tasks clearer, more precise and easier for generative AI systems to interpret.

Prompt Engineering designs, tests and refines prompts to reduce ambiguity and improve the relevance and quality of generative AI outputs.

A generative AI system combines a language model with specialized models, orchestration, tools and workflows to produce and act on results.

Context Engineering designs the information, tools and constraints that allow generative AI to interpret objectives and operate more effectively.

Modern Artificial Intelligence learns from data through supervised, unsupervised and reinforcement learning to build models that generalize to new situations.