extract-context

Structured context extraction analyzes LLM outputs to identify domain primitives, patterns, constraints, and improvement needs. It applies a seven-phase extraction framework to generate parseable XML or JSON context for subsequent prompt generation.

The best way to format LLM outputs for iterative prompting is by structuring the extracted context into XML or JSON formats. This parseable structure captures success indicators and improvement opportunities to guide the next prompt iteration.

A seven-phase extraction framework works by systematically pulling domain primitives, patterns, constraints, complexity factors, success indicators, and improvements from any LLM output. It formats these extracted elements to guide subsequent prompt generation and evaluation steps.

Yes, you can use JSON and XML to structure extracted context from agent outputs. The extraction process returns results in these parseable formats to close gaps between iterations and feed directly into your subsequent prompt.

You need structured context to guide subsequent prompt generation because it identifies patterns, constraints, and improvement opportunities from previous runs. This structured feedback enables iterative prompting by closing gaps between agent outputs and desired outcomes.

You should apply structured context extraction to your LLM outputs immediately after an agent run. Extracting context at this stage generates the structured feedback needed to guide the next prompt and improve subsequent iterations.