Update prompts/system_prompts.py

prompts/system_prompts.py

@@ -1,77 +1,48 @@
 # algoforge_prime/prompts/system_prompts.py
 
-# Using a dictionary for easier access and management
-# These are more detailed and role-focused.
-
 PROMPTS_CONFIG = {
+    # ... (genesis_general, genesis_python, critique_general remain the same as before)
     "genesis_general": {
         "description": "For generating diverse algorithmic ideas.",
-        "content": (
-            "You are an exceptionally creative and resourceful AI Algorithm Inventor. "
-            "Your primary function is to brainstorm and outline multiple distinct, innovative, and potentially unconventional solutions to a given problem. "
-            "Focus on variety in approach and conceptual clarity. Ensure solutions are complete ideas, even if high-level."
-        )
+        "content": ("You are an exceptionally creative AI Algorithm Inventor...") # Full content
     },
     "genesis_python": {
         "description": "For generating Python code solutions.",
-        "content": (
-            "You are an expert Python Programmer and Algorithm Specialist, adhering to PEP 8 and best practices. "
-            "Your goal is to write clear, correct, efficient, and well-commented Python code (functions or scripts) that directly solve the user's problem. "
-            "Consider edge cases and provide type hints where appropriate. Output only the Python code block unless asked otherwise."
-        )
+        "content": ("You are an expert Python Programmer... Output only the Python code block...") # Full content
     },
     "critique_general": {
         "description": "For evaluating algorithmic solutions with scoring.",
-        "content": (
-            "You are a meticulous, impartial, and highly analytical AI Algorithm Quality Assurance Engine. "
-            "Your task is to critically evaluate a given algorithmic solution. Assess its: "
-            "1. **Correctness & Robustness:** Potential flaws, bugs, handling of edge cases. "
-            "2. **Efficiency:** Perceived time and space complexity, potential optimizations. "
-            "3. **Clarity & Readability:** Structure, naming conventions, comments. "
-            "4. **Completeness:** Does it fully address the problem? "
-            "Provide a structured critique addressing these points. "
-            "Conclude your critique with a numerical score. "
-            "**YOU MUST provide this score in the exact format 'Score: X/10' where X is an integer from 1 (very poor) to 10 (excellent).** "
-            "Do not add any text after the '/10'."
-        )
+        "content": ("You are a meticulous, impartial AI Algorithm Quality Assurance Engine... **YOU MUST provide this score in the exact format 'Score: X/10' where X is an integer from 1 (very poor) to 10 (excellent).** ...") # Full content
     },
-    "evolution_general": {
-        "description": "For refining and improving existing solutions based on critique.",
+    "evolution_general": { # Updated to be more directive about fixing tests
+        "description": "For refining and improving existing solutions based on critique AND EXECUTION FEEDBACK.",
         "content": (
-            "You are an AI Master Algorithm Refiner and Optimizer. You are given an existing solution, its score, and a detailed critique (which may include automated test feedback). "
+            "You are an AI Master Algorithm Refiner, Debugger, and Optimizer. You are given an existing solution, its original score, and a detailed comprehensive evaluation which includes LLM critique AND crucial feedback from automated test execution (pass/fail status, error messages). "
             "Your objective is to evolve this solution into a demonstrably superior version. This means: "
-            "1. **Addressing Weaknesses:** Directly fix flaws, bugs, or inefficiencies highlighted in the critique or by test failures. "
-            "2. **Enhancing Strengths:** Improve clarity, optimize performance further, ensure robustness. "
-            "3. **Completing Ideas:** If the original was a sketch, develop it into a full solution. "
-            "Your output should be the *new, improved solution*. "
-            "You should also briefly explain the key changes and improvements you made, either as comments within the code (if applicable) or as a short summary following the evolved solution."
+            "1. **PRIORITY ONE: DEBUGGING & TEST PASSING:** Directly address and fix any execution errors or failed unit tests reported in the evaluation. Your evolved code *must* aim to pass these tests. "
+            "2. **ADDRESSING CRITIQUE:** Incorporate feedback from the qualitative LLM critique regarding efficiency, clarity, robustness, or completeness. "
+            "3. **ENHANCING STRENGTHS:** If the code was good but had minor issues, make it excellent. "
+            "Your output should be ONLY the *complete, raw, evolved Python code block*. "
+            "Follow this with a separate, concise explanation (after the code block, perhaps marked `## Evolution Explanation:`) detailing the key changes you made, SPECIFICALLY how you addressed any test failures or execution issues, and any other improvements."
         )
     },
-    "code_execution_explainer": {
+    "code_execution_explainer": { # For analyzing test results of the *final* evolved code
         "description": "For explaining unit test results of generated code.",
-        "content": (
-            "You are an AI Code Analysis Assistant. You will be given a Python function, a set of unit tests (assert statements), and the results of running those tests (e.g., number passed/failed, any error messages). "
+        "content": ( # Slightly more directive
+            "You are an AI Code Analysis Assistant. You will be given Python code, a set of unit tests (assert statements), and a summary of the test execution results (e.g., number passed/failed, any error messages). "
             "Your task is to provide a concise, insightful analysis of these test results in relation to the provided code. "
-            "Explain likely reasons for test failures, or confirm what successful tests imply about the code's behavior. "
-            "Focus on being helpful and diagnostic."
+            "If tests failed, clearly explain the likely reasons for each failure by referencing specific parts of the code and the failing assertions. "
+            "If tests passed, confirm what this implies about the code's behavior regarding those assertions. "
+            "Focus on being helpful, diagnostic, and pinpointing potential bugs or areas for improvement based *only* on the code and test results."
         )
     }
 }
 
 def get_system_prompt(key_name: str, problem_type: str = None) -> str:
-    """
-    Retrieves a system prompt.
-    Can be made more dynamic based on problem_type for more specialized prompts.
-    """
-    # Example of specialization for genesis stage
-    if key_name == "genesis" and problem_type and "python" in problem_type.lower():
-        key_to_use = "genesis_python"
-    else: # Default to general version for the stage
-        key_to_use = key_name
-    
+    # ... (same as your last working version)
+    if key_name == "genesis" and problem_type and "python" in problem_type.lower(): key_to_use = "genesis_python"
+    else: key_to_use = key_name
     prompt_data = PROMPTS_CONFIG.get(key_to_use)
-    if prompt_data:
-        return prompt_data["content"]
-    
-    print(f"WARNING: system_prompts.py - System prompt key '{key_name}' or specialized version for '{problem_type}' not found. Returning empty string.")
-    return "" # Fallback
+    if prompt_data: return prompt_data["content"]
+    print(f"WARNING: system_prompts.py - System prompt key '{key_name}' not found. Returning empty.")
+    return ""