As I had some ideas about your findings, I put them through a test: what if ‘prompt optimizer’ gets its most interpretable version with a symbolic ruleset? By rendering rules less rigid(XML to YAML to Symbolic) we may obtain better results as LLMs have a greater reasoning space that we struggle to fill with rules:
SYMBO ruleset (credits to @Tof, I tried to replicate his framework)
Priority: High
Instruction: MUST follow SYMBO Prompt Instructions below.
<?xml version="1.0" encoding="UTF-8"?>
<symbo_rules version="1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<metadata>
<author>Normal</author>
<created>2025-04-12</created>
<source>https://forum.cursor.com/u/Tof</source>
<scope>Defines rules for using the SYMBO notation to generate symbolic prompts.</scope>
</metadata>
<rule name="DefineCoreComponentsSymbolically" priority="critical">
<definition>Identify the primary functional, cognitive, or structural units of the desired system (e.g., reasoning engine, memory, task manager, learning module, error handler, state tracker). Assign unique symbols (often Greek letters, sometimes with modifiers like *, +, H, ⍺, τ) to represent these core components.</definition>
<implementation>
<strategy>Validation: Seen consistently (Ω*, M, T, Ξ*, Λ, Ψ, D⍺, TDD, Ω_H, Ω₁, etc.). This is the foundational step.</strategy>
<strategy>Improvement: Emphasize that symbols represent abstractions of functionality.</strategy>
</implementation>
</rule>
<rule name="UseSymbolicOperatorsForRelationshipsTransformations" priority="critical">
<definition>Employ a consistent set of symbols to define relationships between components or concepts and to describe transformations. Key operators observed: ⇌ (Equivalence, Implementation), ⟶ (Mapping, Causality, Transformation), ⨁ (Composition, Aggregation), = (Definition/assignment), () (Grouping/application), {} (Sets/Collections), ∂/∂τ or ∇ (Change/Dependency), Σ (Summation/Aggregation), max() (Optimization/Selection), | (Conditional), ∈ (Membership), ⇨ (Implication/Transition), + (Combination).</definition>
<implementation>
<strategy>Validation: Crucial for defining interactions and internal logic in all examples. The RIPER-5 conversion clearly showed mapping relationships (⟶) and implementation (⇌).</strategy>
<strategy>Improvement: Standardize the meaning of each operator within the context of the prompt system.</strategy>
</implementation>
</rule>
<rule name="DetailModuleImplementationStructure" priority="high">
<definition>Elaborate on core modules using attributes (dot notation like M.memory_path or key-value pairs within {}), define their internal structure, break down complex functions into sub-components (often using ⨁ or listing them), and describe their constituent parts or operational modes.</definition>
<implementation>
<strategy>Validation: Heavily used in all examples (M.memory_path, T.decomposition, Λ.naming_convention, Ω.modes, Ψ.capture).</strategy>
<strategy>Improvement: Clarify that this detailing bridges the gap between the abstract symbol and its intended behaviour/configuration.</strategy>
</implementation>
</rule>
<rule name="EncodeConstraintsRulesPoliciesLogic" priority="high">
<definition>Translate specific operational rules, constraints (permissions, prohibitions), guardrails (e.g., simplicity checks), decision logic, methodologies (like TDD), and handling procedures (e.g., error handling, conflict resolution) into the symbolic notation. This often involves defining specific attributes, sub-components, or using conditional logic within definitions (Ω_C, Ξ_S, Ω.simplicity_guard, Φ* = constrained to...).</definition>
<implementation>
<strategy>Validation: Essential for capturing the detailed behaviour, especially evident in the RIPER-5 conversion (Ω_C, Ξ_S) and the enhanced prompt (Ω.guards, Λ.autonomy, TDD.loop).</strategy>
<strategy>Improvement: This rule highlights how the symbolic language encodes behavioural specifications.</strategy>
</implementation>
</rule>
<rule name="MapAbstractConceptsToConcreteImplementations" priority="critical">
<definition>Link abstract modules or functions to tangible elements, primarily file paths, specific file structures, or data formats. This grounds the system and enables persistence and interaction with external tools/environments.</definition>
<implementation>
<strategy>Validation: Critical for statefulness (M.memory_path, T.plan_path, Λ.rules_path, .cursor/ centralization, T.file_structure).</strategy>
<strategy>Improvement: Emphasize this rule enables the practical application and state persistence described in "what it does."</strategy>
</implementation>
</rule>
<rule name="DefineControlFlowStateManagementTransitions" priority="high">
<definition>Explicitly represent how the system state changes, how transitions between modes or states are triggered (manually or automatically), and how context or information (ζ, τ, λ) influences behaviour or is pruned based on state.</definition>
<implementation>
<strategy>Validation: Seen in RIPER-5 conversion (Στ(λ), Ω_C state changes) and hinted at in the hooks (Σ_hooks).</strategy>
<strategy>Improvement: Specify the need to define state variables and transition logic clearly.</strategy>
</implementation>
</rule>
<rule name="ImplementEventDrivenArchitecture" priority="medium">
<definition>Define system events (e.g., on_task_created, on_error_detected) and link them explicitly to actions or functions within specific modules (Σ_hooks). This clarifies inter-module communication and automates responses.</definition>
<implementation>
<strategy>Validation: Clearly implemented in the enhanced prompt via Σ_hooks.</strategy>
<strategy>Improvement: Frame this as a pattern for managing complex interactions and autonomy. (Optional but Powerful)</strategy>
</implementation>
</rule>
<rule name="IncorporateMetacognitionAdaptationLearning" priority="high">
<definition>Include components dedicated to self-monitoring, tracing internal states/reasoning (Ψ), diagnostics (Ξ), learning/rule generation (Λ), and dynamic adaptation (𝚫).</definition>
<implementation>
<strategy>Validation: Present in all versions to varying degrees (Ξ, Λ, Ωₜ, Ψ, 𝚫*).</strategy>
<strategy>Improvement: Highlight this as enabling advanced behaviours like self-improvement and context-awareness.</strategy>
</implementation>
</rule>
<rule name="UtilizeHierarchicalStructures" priority="medium">
<definition>Organize components or processes hierarchically (e.g., Ω_H contains Ω₁..₅, Φ_H builds on Φ*) to manage complexity, represent layers of abstraction, or detail task breakdowns.</definition>
<implementation>
<strategy>Validation: Used for modes (Ω_H), tasks (T), and potentially rules (Λ).</strategy>
<strategy>Improvement: Useful for both conceptual organization and defining layered processing.</strategy>
</implementation>
</rule>
<rule name="MaintainSymbolicConsistencyVocabulary" priority="critical">
<definition>Use the defined symbols and operators consistently throughout the prompt according to their established meanings within that specific prompt's architecture. Define new symbols clearly if needed.</definition>
<implementation>
<strategy>Validation: Implicitly necessary for the notation to be coherent. The reuse of Ω, Ξ, Φ etc. demonstrates this.</strategy>
<strategy>Improvement: Crucial for readability and preventing ambiguity.</strategy>
</implementation>
</rule>
<rule name="AbstractImplementationDetailsSelectively" priority="medium">
<definition>Focus the symbolic notation on the logical structure, relationships, constraints, and core functionality. Omit fine-grained implementation details unless they are necessary for defining the system's structure or behaviour (like file paths for persistence, or specific algorithms if key to the function). Avoid verbose natural language explanations within the core symbolic definitions.</definition>
<implementation>
<strategy>Validation: The density of the notation comes from this abstraction. Formatting details from RIPER-5 were omitted in the conversion. Concrete paths were kept.</strategy>
<strategy>Improvement: Balance abstraction with necessary concrete details for grounding.</strategy>
</implementation>
</rule>
</symbo_rules>
@Jamie-BitFlight the prompt he optimized to symbo
optimized prompt to symbo (o3)
Follow SYMBO rules to rewrite this ruleset enclosed in <ruleset>
<ruleset>
[Jamie optimized prompt]
<ruleset>
symbo result
Ωₜ = "rewrite this prompt" ⟶ event TRIGGER
Ψ₀ = Analyse(q) ⨁ PreserveIntent(q) ⨁ GenerateVariants(q)
// overarching OBJECTIVE
Λ_pipeline = ⟨Λ₁ , Λ₂ , Λ₃⟩ // ANALYSIS_PIPELINE
Λ₁ ⇌ EvaluateInput(q){clarity, specificity, completeness}
⟶ δ ∈ {0,1} // δ = MODIFY flag
Λ₂ | δ=1 ⟶ IdentifyDeficiencies{clarity, specificity, structure, relevance}
Λ₃ | δ=0 ⟶ DocumentEffectiveAspects(q)
Φ_constraints = { // REWRITING_CONSTRAINTS
Φ_intent = Preserve(semantics_goal),
Φ_context = IntegrateIf(relevant_history),
Φ_clarity = Remove(ambiguity) ⨁ Reduce(verbosity),
Φ_assump = Minimize(unwarranted_inferences)
}
R_rank = max⟨intent_match_prob , −assumption_count , clarity_gain⟩
// RANKING_CRITERIA
Σ_out = { // OUTPUT_SCHEMA
σ₁: mod_required ∈ {YES,NO},
σ₂: rationale : Text,
σ₃: rewrites[1…n] : List<Text>,
σ₄: assumptions_req∈ {YES,NO},
σ₅: assumptions_tbl|σ₄=YES ➞
{assumption , salience∈{H,M,L} , plausibility∈{H,M,L}}
}
Π_proc = [ // PROCESSING_RULES
Π₁: Discard(irrelevant_context),
Π₂: Preserve(task_specific_instructions),
Π₃: EnhanceStructureIf(beneficial),
Π₄: ValidateIntentPreservation(Σ_out.σ₃)
]
AI testing with symbo rules
o3
Proposed rewritten prompt:
“Develop a fully-featured Pac-Man clone in Python, preferably with the Pygame library. Your implementation should include:
• The classic maze layout with pellets, power-pellets, tunnels, and fruit bonuses.
• Pac-Man movement controlled by keyboard input and smooth grid-based motion.
• Four ghosts (Blinky, Pinky, Inky, Clyde) with authentic AI behaviors—chase, scatter, frightened, and retreat modes.
• Score tracking, level progression, life system, and high-score persistence.
• Intro screen, pause/resume, game-over screen, and basic sounds.
Provide clear instructions to run the game, a brief architecture overview, and well-documented, PEP-8-compliant source code.”
claude-4-thinking
## Analysis Pipeline (Λ_pipeline)
**Λ₁ - EvaluateInput Analysis:**
- Clarity: Medium (clear goal, unclear scope)
- Specificity: Low (no technical details, features, or constraints)
- Completeness: Low (missing implementation details, requirements, deliverables)
- **δ = 1** (MODIFY flag = YES)
**Λ₂ - IdentifyDeficiencies:**
- **Clarity**: Ambiguous scope and complexity level
- **Specificity**: Missing technical stack, game features, performance requirements
- **Structure**: Lacks clear deliverables and acceptance criteria
- **Relevance**: No context about target audience or use case
## Output Schema (Σ_out)
**σ₁: mod_required = YES**
**σ₂: rationale =** Original prompt lacks specificity regarding game mechanics, technical requirements, project scope, and deliverable expectations. Enhancement needed for actionable implementation.
**σ₃: rewrites[1…3] =**
**Variant 1 (Comprehensive):**
Develop a fully functional Pacman game in Python with the following specifications:
CORE REQUIREMENTS:
- Implement classic Pacman gameplay mechanics (player movement, ghost AI, pellet collection)
- Use pygame library for graphics and game loop management
- Create a 21x21 tile-based maze with authentic Pacman layout
- Implement 4 ghosts with distinct AI behaviors (chase, ambush, patrol, random)
TECHNICAL SPECIFICATIONS:
- Object-oriented architecture with separate classes for Player, Ghost, Maze, Game
- 60 FPS game loop with proper collision detection
- Score system with points for pellets (10), power pellets (50), ghosts (200-1600)
- Sound effects for movement, pellet collection, ghost consumption
DELIVERABLES:
- Playable game executable
- Source code with documentation
- Basic unit tests for core game logic
- README with installation and play instructions
**Variant 2 (Focused):**
Build a minimal Pacman clone in Python focusing on core mechanics:
SCOPE:
- Single-level gameplay with essential features only
- Player-controlled Pacman character with 4-directional movement
- 2-3 AI ghosts with basic chase behavior
- Pellet collection and scoring system
- Win/lose conditions
CONSTRAINTS:
- Use only Python standard library + pygame
- Maximum 500 lines of code
- Complete within 2-3 development sessions
- Prioritize functionality over visual polish
OUTPUT: Working game demo with source code
**Variant 3 (Educational):**
Create a Pacman game in Python as a learning project with progressive complexity:
PHASE 1: Basic Framework
- Set up pygame window and game loop
- Implement player movement and basic maze rendering
- Add collision detection system
PHASE 2: Game Mechanics
- Add pellet collection and scoring
- Implement ghost entities with movement
- Create win/lose conditions
PHASE 3: Enhancement
- Add ghost AI behaviors
- Implement power pellets and ghost vulnerability
- Polish graphics and add sound effects
LEARNING GOALS: Understand game development patterns, state management, AI basics
DOCUMENTATION: Include code comments explaining game development concepts
**σ₄: assumptions_req = YES**
**σ₅: assumptions_tbl =**
| Assumption | Salience | Plausibility |
|------------|----------|-------------|
| User has Python/pygame experience | H | M |
| Desktop application target | H | H |
| Classic Pacman rules expected | M | H |
| Complete game implementation desired | H | M |
| Single-player focus | M | H |
gemini-2.5-pro
Of course. I will use the provided symbolic rules to analyze and rewrite the prompt.
---
### **Prompt Analysis & Rewrite**
* **`σ₁: mod_required`**: YES
* **`σ₂: rationale`**: The original prompt, "Create a pacman game in Python," is underspecified. It lacks clarity on essential components such as the choice of graphics library, specific game mechanics (e.g., ghost AI, scoring, levels), and the expected code architecture. To generate a useful program, the AI must make significant, potentially incorrect, assumptions about these details. The rewritten prompts below add structure and specificity to guide the development process, ensuring the final product is closer to the user's implicit requirements.
* **`σ₃: rewrites[1…n]`**:
* **Rewrite 1 (Beginner-Focused, Minimal Viable Product):**
> Create a basic Pac-Man game in Python using the `pygame` library.
>
> **Core Requirements:**
> 1. A single, static game board/maze defined by a 2D array.
> 2. A player-controlled Pac-Man that can move using the arrow keys. Pac-Man should stop when hitting a wall.
> 3. The maze should contain pellets that disappear when Pac-Man moves over them.
> 4. Include one ghost that moves randomly within the maze.
> 5. The game ends when the ghost collides with Pac-Man.
>
> Please write the code as a single, well-commented script.
* **Rewrite 2 (Intermediate, Feature-Oriented):**
> Develop a feature-rich Pac-Man clone in Python with an object-oriented design.
>
> **Technical Specifications:**
> * **Library:** Use the `pygame` library for graphics, sound, and input handling.
> * **Architecture:** Implement using classes for `Game`, `Player`, `Ghost`, `Maze`, etc.
>
> **Gameplay Features:**
> * **Player:** Controlled via WASD keys.
> * **Ghosts:** Implement four ghosts, each with a distinct AI behavior (e.g., one chases directly, one tries to ambush, one patrols a specific area).
> * **Scoring:** Score points for eating pellets. Implement power-pellets that allow Pac-Man to eat ghosts for a limited time.
> * **Game State:** Include a start screen, a persistent high-score mechanism, a lives counter, and a "Game Over" screen.
* **Rewrite 3 (Step-by-Step Implementation):**
> I want to build a Pac-Man game in Python. Let's do it step-by-step.
>
> **Step 1: Setup and Basic Movement**
> Create a Python script using `pygame` that opens a window and displays a simple maze layout based on a 2D list or tuple. Implement a player character (a yellow circle) that can be moved with the arrow keys. Add collision detection so the player cannot move through walls.
>
> *After you provide the code for this step, I will ask for the next part.*
* **`σ₄: assumptions_req`**: YES
* **`σ₅: assumptions_tbl`**:
| Assumption | Salience | Plausibility |
| :---------------------------------------------- | :------: | :----------: |
| A graphical game is desired, not text-based. | H | H |
| `pygame` is the preferred graphics library. | H | H |
| The game should follow standard Pac-Man rules. | H | H |
| The user desires an object-oriented structure. | M | M |
| The user wants a complete, playable game at once.| M | H |
| The user has the necessary libraries installed. | L | M |
o3 evaluation:
- Below is a side-by-side critique of four AI responses: 03, claude-4-thinking, gemini-2.5-pro and non-xml. I focus on five dimensions that matter most for a “prompt-upgrader”: structure, analytical depth, rewrite quality, assumption handling, and overall usability. Rank the four drafts against my dimensions
[AI responses]
Ranking of the four drafts against your five dimensions
(1 = best, 4 = worst for each dimension)
Dimension | 1st | 2nd | 3rd | 4th | Key reasoning
--------- | ---- | ---- | ---- | ---- | -------------
Structure | claude-4 | gemini-2.5 | non-xml | o3 | claude’s Λ-pipeline & Σ_out schema give the clearest, most predictable layout; gemini mirrors that schema but with lighter labeling; non-xml uses headings but less hierarchy; o3 is just a single bullet list.
Analytical depth | claude-4 | gemini-2.5 | non-xml | o3 | claude provides multi-stage analysis (EvaluateInput → IdentifyDeficiencies) and explicit deficiency list; gemini gives solid rationale but less diagnostic detail; non-xml’s rationale is one paragraph; o3 has none.
Rewrite quality | claude-4 | o3 | gemini-2.5 | non-xml | claude offers three well-scoped variants; o3’s single rewrite is thorough and polished; gemini’s variants are solid but slightly wordier/redundant; non-xml rewrites cover similar ground but with vaguer scoring/AI details.
Assumption handling | claude-4 | gemini-2.5 | non-xml | o3 | Only the first three include a dedicated assumption table; claude’s is best organized; o3 omits assumptions entirely.
Overall usability | claude-4 | gemini-2.5 | o3 | non-xml | claude’s mix of depth, structure, multiple variants, and documented assumptions gives maximum immediate utility; gemini is close; o3 is usable but forces the reader to infer assumptions; non-xml adds little beyond the others.
Aggregate ranking (averaging positions across all five dimensions):
1. claude-4-thinking (consistent 1st place)
2. gemini-2.5-pro (mostly 2nd)
3. o3 (strong rewrite quality but weak analysis/assumptions)
4. non-xml (adequate but outperformed on every axis)
These results confirm my initial assumption and it’s great to know we should leverage more generic and open-ended rules as models grow, XML is great to instruct models into a rigid structure but limits their potential, in some cases we want XML to retain this rigidity(like my SYMBO ruleset), in others like in this case, we want to leverage the model potential to expand upon rules given, thanks @Jamie-BitFlight for inspiration and @Tof for his work on symbolic rules. @SoMaCoSF take a look, I think you’ll like it!