Methodology for Human-AI Collaboration
This page provides a glimpse into the approach used to guide AI-assisted development, demonstrating how iterative, intentional interactions can shape outputs while maintaining fidelity to the creator's vision.
Steps to Elicit Targeted AI Output
- Persistent clarity about constraints: Clearly communicate what the AI should and should not assume.
- Explicit corrections: Intervene when outputs include unwanted assumptions or content, redirecting the AI.
- Providing raw conceptual material: Supply thoughts, ideas, or shorthand directly, forming the “DNA” for translation.
- Metaphors for process: Use conceptual analogies (e.g., mRNA for DNA) to clarify the translation role of AI.
- Specifying emotional and factual balance: Distinguish between desired tone and factual content to guide output.
- Controlling perspective and language: Explicitly define pronouns, narrative perspective, and style constraints.
- Iterative, selective refinement: Stepwise edits and confirmations to progressively align outputs with intended vision.
- Final framing with concrete output format: Specify the structure or format required (e.g., HTML, user-facing text).
Summary
The core strategy is moving the AI from an “assume and generate” model to a “translate exactly what is provided” model. By providing raw conceptual material, clearly defining constraints, and iteratively refining outputs, the developer ensures that the AI produces usable, coherent, and accurate content without introducing external assumptions. This methodology supports precise, controlled human-AI collaboration while preserving the creator's intent.