Systematic Prompting Methodology

Overview of the Approach

The systematic prompting approach involves repeatedly asking an AI system to identify the greatest challenge to a proposed solution or approach in advancing AI intelligence. This method creates a chain of increasingly specific challenges, drilling down from high-level concepts to concrete, actionable problems.

Key Components of the Approach

Initial Broad Question

Start with a broad question about approaches to advancing AI intelligence (e.g., "What is the most promising non-scaling approach to making AIs more intelligent?")
Iterative Challenge Identification

After each answer, ask the follow-up question: "In one sentence, what is the greatest challenge to this following approach: (last answer)?"
Progressive Specificity

Each iteration narrows the focus and increases specificity, eventually reaching fundamental challenges that require targeted research and development.
Termination Point

The process continues until reaching a challenge that represents a fundamental limitation or requires a breakthrough that cannot be easily decomposed further.

Example Application

The following example demonstrates the systematic prompting approach applied to the question of integrating symbolic reasoning with neural networks:

Initial Response

"Integrating symbolic reasoning capabilities with the pattern-matching strengths of neural networks is widely considered a very promising non-scaling approach to advance AI intelligence."

First Challenge

"The greatest challenge lies in seamlessly integrating the fundamentally different representations and processing methods—continuous vector spaces for neural networks versus discrete symbols and rules for symbolic AI—in a way that is both effective and scalable."

Second Challenge

"Devising the specific mathematical frameworks and algorithms needed to actually reconcile continuous optimization with discrete logical operations is the core difficulty presented by that integration challenge."

Third Challenge

"The inherent complexity and lack of established theoretical foundations for unifying fundamentally different continuous and discrete mathematical paradigms pose the greatest challenge to devising those specific frameworks and algorithms."

Fourth Challenge

"Overcoming that inherent complexity and lack of established foundations requires generating fundamental scientific breakthroughs or entirely new mathematical paradigms, the path to which is intrinsically difficult and unknown."

Fifth Challenge

"Fundamental breakthroughs rely on unpredictable creative insights that go beyond current understanding, making their deliberate generation inherently difficult and not directly addressable by methodical approaches alone."

Benefits of the Approach

Increased Specificity: Transforms broad, abstract challenges into specific, concrete problems.
Research Direction: Helps identify precise areas where research efforts should be concentrated.
Task Delegation: Enables better delegation of research tasks by clearly defining specific challenges.
Breakthrough Identification: Highlights where fundamental breakthroughs are needed versus incremental improvements.
Systematic Exploration: Provides a structured method for exploring the problem space of AI advancement.

Potential Limitations

Linearity: The approach follows a single chain of challenges, potentially missing parallel or alternative challenge paths.
AI System Limitations: The quality of insights depends on the capabilities of the AI system being prompted.
Subjective Prioritization: The identification of the "greatest" challenge involves subjective judgment.
Depth vs. Breadth: Focuses on depth of a single challenge path rather than breadth of multiple challenges.

Potential Enhancements

Multiple Starting Points: Apply the approach from different initial questions to cover more ground.
Branching Challenges: Ask for multiple challenges at each step rather than just the greatest one.
Cross-Validation: Use multiple AI systems to validate and compare challenge identifications.
Human Expert Integration: Combine AI-generated challenges with human expert insights.
Automation: Develop tools to automate this process across multiple AI systems and starting points.