The RISEN framework (Role, Input, Steps, Expectation, Narrowing) serves as a critical structural scaffolding for high-precision prompt engineering, where the Narrowing component specifically functions as the designated "negative prompt" vector. By explicitly utilizing the Narrowing phase to define exclusionary criteria like what the AI must not do, say, or assume users effectively reduce the model's stochastic "search space," minimizing hallucinations, generic filler, and off-topic digressions.
This pre-emptive exclusion allows the AI to channel its computational resources solely toward the desired parameters defined in the Role, Input, and Steps phases, thereby transforming vague generative capabilities into rigorous, constrained outputs suitable for high-stakes academic or commercial environments.
The following table outlines how the RISEN framework, particularly its narrowing capabilities, can be leveraged across academic and commercial sectors:
| Dimension | Academic Leverage | Commercial Leverage |
|---|---|---|
| Data Integrity & Precision | Hallucination Reduction: Use Narrowing to explicitly forbid the fabrication of citations or the use of non-peer-reviewed sources, ensuring literature reviews remain factually grounded. | Operational Reliability: In automated workflows like JSON extraction, Narrowing prevents the inclusion of conversational filler ("Here is your data"), ensuring code pipelines don't break. |
| Bias & Tone Control | Objective Analysis: Define negative constraints to exclude emotive language, persuasive rhetoric, or first-person bias, enforcing a neutral, empirical tone required for scholarly writing. | Brand Consistency: Commercial prompts can use Narrowing to ban specific words like "delve," "cutting-edge" or competitors' names, ensuring all output aligns strictly with the company's voice and legal guidelines. |
| Reproducibility & Standardization | Methodological Rigor: By standardizing Steps and Narrowing constraints like "Do not summarize; extract raw data only," researchers can ensure AI-assisted qualitative coding is reproducible across different datasets. | Customer Service Safety: "Negative prompts" in the Narrowing phase can prohibit chatbots from offering medical/legal advice or promising refunds, protecting the business from liability. |
| Resource Efficiency | Focused Inquiry: Narrowing the scope like "Limit analysis to post-2020 publications only" saves review time and prevents the model from diluting relevant findings with outdated information. | Cost Reduction: By explicitly constraining length and scope like "Do not exceed 100 words," "Do not explain the process," businesses reduce token consumption and API costs while speeding up response times. |
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