Building an LLM Narrative Engine: How Rules and Interpolation Are Parsed

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A developer working on an LLM-powered narrative engine has detailed the parsing logic used in Part 4 of their ongoing series. The parser processes rule expressions in a fixed format — name, condition, and action — splitting them using delimiters like 'if' and '->' without evaluating them at parse time. Evaluation is deferred to runtime because state values, such as emotion levels or corruption indices, change dynamically during conversation. The parser also handles interpolation syntax, recognizing placeholder tokens in action strings for later substitution. Additionally, a mutex group mechanism is supported, allowing the parser to store group labels so the runtime can ensure only the first matching rule in a group fires.

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