Adaptive Prompt Systems

An adaptive prompt system selects and configures prompts based on the characteristics of the incoming research request. Instead of the researcher choosing a prompt manually, the system evaluates the request — scope, domain, complexity, urgency — and assembles the appropriate prompt chain automatically. This is where prompt engineering becomes prompt infrastructure.

The architecture has three components. A classifier that evaluates the incoming research request and maps it to a domain, task type, and complexity level. A selector that chooses the appropriate prompt chain from the library based on the classification. A configurator that fills in the context, constraints, and format directives based on the specific request parameters. The researcher provides the research question and the relevant context. The system provides the prompt architecture. The result is consistent quality regardless of which researcher initiates the request.

1. Request Classification Parse the incoming research request to determine domain (competitive, technology, market, customer), task type (collection, analysis, synthesis), and complexity (single-prompt, chain, multi-session). Classification can be rule-based for well-defined domains or AI-assisted for novel requests.
2. Prompt Chain Selection Based on classification, select the appropriate prompt chain template from the library. A competitive threat assessment uses a different chain than a technology evaluation. The chain selection determines the cognitive workflow — how many stages, what each stage optimizes for.
3. Dynamic Configuration Fill the selected prompt chain with request-specific parameters: the target company, the time window, the geographic scope, the output format preference. The structural architecture stays constant. The content parameters change per request. This is how the system produces consistent methodology with customized output.