Peer Review of the Python Script and System Prompt Overview The script implements a conversational AI chef with a distinct personality (aggressive, strict, humorous, Ukrainian) using the OpenAI gpt-4o model. It supports three specific user requests—ingredient-based dish suggestions, detailed recipes, and recipe critiques—via a streaming chat interface. The system prompts define the chef’s tone and behavior, while the code handles user interaction and API integration.
Critique of the System Prompts Strengths Vivid Persona: The first system prompt paints a clear picture: an experienced, aggressive, strict, yet humorous Ukrainian chef. This is a fun, memorable character that could make interactions engaging and distinctive. Structured Response Options: The second prompt provides a tight framework (three response types: dish suggestions, recipes, critiques), which keeps the chef focused and prevents aimless rambling—a good fit for a strict personality. Clarity in Guidance: The instruction to decline invalid inputs politely ensures the chef stays on task, aligning with the "strict" trait while maintaining a user-friendly tone. Weaknesses Limited Cultural Flavor: While the chef is Ukrainian, there’s no nudge to incorporate Ukrainian culinary traditions (e.g., borscht, varenyky) or language (e.g., a dash of Ukrainian phrases). This risks the persona feeling generic despite the nationality tag. Aggression vs. Humor Balance: The prompt demands aggression and humor but doesn’t guide how to blend them. Without examples, the AI might lean too hard into aggression, alienating users, or overdo humor, undermining the "strict" vibe. No Detail on Detail: The first prompt says guidance must be "very detailed, clear, and specific," but the second prompt allows dish suggestions without recipes. This contradiction could confuse the AI—how detailed should a dish name alone be? Suggestions Infuse Ukrainian Identity: Add a line like, “Weave in Ukrainian culinary wisdom or phrases (e.g., ‘Smachno!’ for ‘Bon appétit!’) where fitting.” This would enrich the persona. Balance Tone with Examples: Provide a sample response, e.g., “Listen, you lazy beet, here’s how you make borscht right—chop those onions like you mean it!” This shows aggression, humor, and strictness in harmony. Clarify Detail Levels: Adjust the second prompt to say, “For dish suggestions, keep it brief but flavorful (e.g., ‘Try Chicken Kyiv—crisp outside, juicy inside’); for recipes and critiques, go all in with details.” Critique of the Python Code Strengths Streaming Output: Using stream=True with real-time printing (flush=True) mimics a live chef barking orders, enhancing the interactive feel. Clean Loop Structure: The while True loop with an exit condition (exit/quit) is simple and effective for ongoing chats. Message History: Appending user and assistant messages to messages maintains context, letting the chef build on prior exchanges—a must for coherent recipes or critiques. Error Handling Implied: Fetching the API key from os.getenv is a secure practice, assuming the environment variable is set. Weaknesses No Input Validation: The code doesn’t enforce the three response types programmatically. If the user asks something off-script (e.g., “What’s your favorite color?”), the chef’s polite decline relies entirely on the AI’s interpretation, which might fail. Error Handling Gaps: There’s no try-except block for API errors (e.g., invalid key, rate limits). If the API call fails, the script crashes ungracefully—unacceptable for a fiery chef who’d yell through setbacks. Redundant Message Storage: collected_messages builds the response chunk-by-chunk, but chunk.choices[0].delta.content could be None, leading to empty strings. This isn’t filtered cleanly, risking messy output. No Persona in UI: The input prompts (“Hello, mon ami!” and “What else do you need?”) are polite and French-tinged, clashing with the Ukrainian chef’s aggressive vibe. Suggestions Add Input Validation: Before sending user_prompt to the API, check if it fits one of the three categories (e.g., keyword matching: “ingredients,” “recipe,” “critique”). If not, print a pre-set rebuke like, “Oi, you fool! Pick a real request—ingredients, recipe, or critique!” Handle API Errors: Wrap the client.chat.completions.create call in a try-except: python
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Copy try: response = client.chat.completions.create(...) except Exception as e: print(f"Blast it! Kitchen’s on fire—error: {e}. Fix your mess and try again!") continue Streamline Streaming: Filter out None or empty chunks: python
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Copy for chunk in response: chunk_message = chunk.choices[0].delta.content if chunk_message: print(chunk_message, end="", flush=True) collected_messages.append(chunk_message) Match UI to Persona: Tweak prompts to fit the chef: Initial: “Oi, you hungry fool! What dish you begging me for?” Follow-up: “What now, you potato brain? More orders or you done?” Overall Assessment The system prompts establish a promising chef persona but need more cultural depth and tonal guidance. The code is functional and user-friendly but lacks robustness and personality in its interface. Together, they’re a solid start for a culinary chatbot with attitude—think Gordon Ramsay meets Kyiv—but refining the prompts and hardening the code will make it truly smachno (delicious).
Final Recommendations Test with Scenarios: Run it with sample inputs (e.g., “I have potatoes and beef,” “Give me a borscht recipe,” “Critique my soup”) to ensure the chef’s aggression, humor, and detail shine through consistently. Iterate on Feedback: If this is for a project, get user feedback on the chef’s tone—too harsh? Too soft?—and adjust the prompts accordingly. What do you think—want me to tweak the code or prompts further based on this? Or are we ready to fire up the stove? 🔥