| Aspect | Prompt Engineering | Layered Disclosure Protocol |
|---|---|---|
| Objective | To craft precise, efficient instructions to elicit a desired response or output from an AI. | To reveal information incrementally, testing responses, uncovering misalignments, and refining communication. |
| Approach | Direct and task-oriented: The goal is to provide all necessary context upfront for optimal output. | Iterative and dynamic: Information is strategically layered over time, observing and adapting to recipient responses. |
| Focus | Primarily on AI’s understanding and execution of the prompt. | Primarily on the recipient’s reactions, accountability, and relationship dynamics. |
| Use Case | Ideal for single-step outputs or well-defined, contained tasks. | Ideal for complex, multi-layered scenarios requiring systemic understanding and trust-building. |
| Communication Style | Often includes detailed instructions with explicit boundaries and examples. | Begins with minimal disclosure, focusing on fostering alignment and surfacing systemic issues. |
| Evaluation Criteria | Success is measured by how closely the AI’s output aligns with the intended result. | Success is measured by the recipient’s response, understanding, and the insights revealed through iteration. |
-
Prompt Engineering:
- Provides all the necessary context upfront.
- Aims for a single optimal output based on the given information.
-
LDP:
- Begins with minimal context, building incrementally.
- Uses each disclosure to test for alignment and gather insights before deciding the next step.
-
Prompt Engineering:
- Typically a one-way interaction (AI receives and responds).
- Emphasis is on efficiency and clarity of the initial instruction.
-
LDP:
- A dynamic, iterative process where the recipient’s reaction shapes the next step.
- Emphasis is on observing and understanding the recipient’s perspective.
-
Prompt Engineering:
- Best suited for complicated problems where the solution is known but requires precise execution.
- Example: "Summarise this article in one paragraph."
-
LDP:
- Designed for complex or chaotic problems where solutions emerge through iteration and trust-building.
- Example: "Evaluate systemic biases in this institutional process by testing how recipients respond to layered disclosures."
-
Prompt Engineering:
- Aims to generate a correct or useful output efficiently.
- Success is binary: Did the prompt achieve the desired outcome?
-
LDP:
- Aims to surface insights, uncover misalignments, and refine relationships.
- Success is multi-dimensional, involving progress in understanding, collaboration, and system reform.
-
Prompt Engineering:
- Largely transactional, focusing on task completion.
-
LDP:
- Relational, focusing on fostering trust, accountability, and alignment.
Prompt Engineering and the Layered Disclosure Protocol can complement each other:
- Prompt Engineering could refine individual disclosures within the LDP, ensuring clarity and precision for each layer.
- LDP could test the long-term viability of prompt-driven outputs by introducing iterative feedback, surfacing gaps, and adapting strategies based on context shifts.
Prompt Engineering is ideal for crafting efficient, precise instructions to solve specific, defined problems. The Layered Disclosure Protocol, by contrast, excels in navigating complex, iterative scenarios that require trust-building, misalignment identification, and dynamic adaptation.
Both approaches are valuable but serve distinct purposes—Prompt Engineering focuses on optimising the immediate interaction, while LDP is designed to explore systemic dynamics and build collaborative relationships over time.