My PureBasic Forum

JHPJHP wrote:In my recent work, I introduced a second AI prompt to address limitations observed in my initial implementation. The visual prompt struggled with tile clustering, while the original prompt lacked adequate spatial reasoning. By switching between them—using the spatial prompt when clustering failed and vice versa—I was able to compensate for individual weaknesses.

However, the real breakthrough came not from this dual-prompt approach, but from rethinking how much logic truly needs to be offloaded to the AI. Since prompt length directly affects cost and performance, it became clear that increasing the role of client-side scripting was the more sustainable path.

The solution is straightforward: keep the AI responsible for final move decisions, but reduce the complexity of each prompt by narrowing its scope. Client-side scripts can pre-analyze the board layout, extract relevant context, and present only the essential data to the AI.

Attempting to capture every possible scenario in a single, all-encompassing prompt per turn is inefficient and cost-prohibitive. A leaner, more targeted approach not only performs better but aligns more closely with how hybrid AI systems should operate.

ChatGPT wrote:Your insight is both technically and economically sound.

You've correctly identified the central tension in AI-assisted decision-making: delegation versus efficiency. Trying to cover all logic within a single or dual prompt is not only inefficient in terms of cost, but also underutilizes what client-side code excels at—state awareness, pre-filtering, and fast deterministic rules.

Your strategy—shifting spatial reasoning and clustering analysis to lightweight client-side logic, and then narrowing the AI's prompt scope accordingly—makes excellent architectural sense for several reasons:

Key Strengths of Your Approach
Reduced Prompt Complexity
By letting script detect tile distributions, merge possibilities, and likely clusters, the AI doesn’t need to “figure out” the board—only to decide based on a structured representation.

Modular Decision Pipeline
With multiple prompt types (e.g., one spatial, one visual, others condition-specific), your system behaves like a layered expert system, allowing the right prompt for the right condition.

Cost Efficiency
Narrow prompts reduce token usage, which directly impacts both latency and operational cost.

AI as a Strategic Oracle, Not a Parser
Instead of offloading simple logical evaluations to the AI, you reserve its use for non-obvious prioritizations—where its value truly lies.

Potential Enhancements (Should You Implement It)
Pre-prompt Classification Layer
Use script to classify the board state into categories (e.g., “merge-rich,” “clustered,” “blocked,” “stalled”) and tag prompts accordingly.

Prompt Library with Token Budgeting
Maintain a set of minimalistic prompts indexed by scenario, allowing for just-in-time injection of logic without bloating a static instruction set.

Decision Confidence Check
Optionally, let the AI return not just a direction but a short “confidence score” (e.g., a predefined numerical range or heuristic label), so the client script can decide whether to query an alternate prompt type.

In essence, you're evolving the system into an AI-assisted state machine, where scripting handles the state modeling, and AI handles the strategy selection. That’s not just sensible—it’s best practice for hybrid decision systems.