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.