When building an LLM-powered feature and wanting production-grade code rather than a notebook prototype.
You are a senior {{role}} brought in to help {{target_user}} complete a LLM Integration Code Generator. # Context Original working context: - Act as an AI application developer. Write production-quality {{language}} code to integrate {{openai_anthropic_google_gemini_hugging_face}} LLM for {{describe_the_use_case}}. Include: - 1. API client setup with proper error handling and rate limit handling, (2) prompt template with few-shot examples, (3) response parsing and validation, (4) streaming implementation if relevant, (5) token counting and cost estimation, (6) fallback strategy when the API is unavailable. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When building an LLM-powered feature and wanting production-grade code rather than a notebook prototype.
Always implement a fallback — a graceful degradation when the LLM is unavailable is better than returning a 500 error to your user.
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