Information Supply Patterns
Information Supply covers patterns for assembling the information the model needs to do the task well. Context includes not only background facts and constraints, but also the actual artifacts being worked on, such as source code, documents, or data. These patterns help you decide what to provide, how to structure it, and what to omit so the model doesn't have to guess.
Context Injection
Supplies the background information, state, and references a language model must rely on to perform a task correctly.
Context Reconstruction
Forces the model to deliberately decide what to attend to by regenerating a cleaner, task-relevant context before answering, reducing distraction, bias-copying, and spurious correlations.
Context Scoping
Constrains a model's response by explicitly declaring what information is relevant and what should be ignored, without adding, removing, or rewriting context.
Example-Driven Specification
Specifies desired model behavior by driving the specification through concrete examples rather than abstract rules.
Structural Segmentation
Separates a prompt into clearly defined regions so instructions, inputs, constraints, and reference material are interpreted according to their intended roles rather than inferred from context.