Context Quality
Problems and properties of the input/prompt.
Every interaction with an AI model begins with context — the prompt, the instructions, the history, the data. The quality of that context determines everything that follows. Poor context doesn't just produce poor output; it forces the model to spend expensive reasoning cycles compensating for what should have been clear from the start. These terms name the properties that make context effective or wasteful.
Context Debt
Accumulated ambiguity, redundancy, and disorder in a conversation or prompt.
Context Load
The total volume and complexity of context a model must process in a single call.
Prompt Bloat
Oversized, redundant context that forces unnecessary reasoning.
Context Clarity
A state where the prompt contains only what the model needs — no ambiguity, no redundancy, high signal. The opposite of Context Debt.
Signal Density
The ratio of useful information to total tokens in a prompt. High signal density = low reasoning tax.
Prompt Hygiene
The practice of keeping prompts clean, structured, and free of accumulated noise. Preventive maintenance against Context Debt.
Context Freshness
How current and relevant the context is to the task at hand. Stale context — old instructions, outdated facts — increases reasoning cost even when well-structured.
Structured Context
Context organized with clear hierarchy, roles, and boundaries — making it machine-parseable, not just human-readable. The architectural practice behind Context Clarity.