Output Representation Patterns
Output Representation focuses on shaping the final output into a usable artifact. By default, language models interleave exploration, reasoning, and conclusions in a single stream of text. These patterns introduce structure, constraints, and signals that guide the model away from open-ended exploration and toward a form that can be relied upon, parsed, or acted on.
Output Template
Declares the structure of the model's response in advance, guiding generation toward a stable, consumable artifact rather than an open-ended stream of text.
Semantic Compression
Compresses information by preserving intent and meaning rather than exact wording, enabling substantially larger effective context within fixed model token limits.
Response Tail
Appends a deliberate, structured segment to every model response that contains information secondary to the user's primary goal, ensuring consistent disclosure, guidance, or context regardless of the main content.
Formal Representation
Forces the model to express its output in a rule-governed symbolic system rather than natural language prose, so meaning is carried by structure instead of explanation.
Answer Extractor
Forces a language model to explicitly commit to a final, machine-readable answer after reasoning, separating thinking from decision and reducing ambiguity.