When an LLM integration is producing inconsistent or low-quality outputs and the model choice is not the problem.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Machine Learning & AI Engineering - Use case: Prompt Engineering Toolkit - Source task: - Optimise prompts for {{describe_the_llm_task_classification_summarisation_code_generati}}. Current prompt: {{paste_current_prompt_or_describe_the_task}}. Provide: - 1. chain-of-thought reasoning instruction to improve accuracy - 2. few-shot examples (3-5) showing ideal input-output pairs - 3. output format specification (JSON schema, structured text) to make parsing reliable - 4. guardrails to prevent off-topic or harmful outputs - 5. A/B test plan for the top 3 prompt variations to evaluate # Goal Improved prompt with CoT instructions, few-shot examples, output schema, guardrails, and an A/B test plan for variations. # Constraints - Produce a complete, usable first draft in one response. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output Improved prompt with CoT instructions, few-shot examples, output schema, guardrails, and an A/B test plan for variations.
{{double-curly}} with your real context.When an LLM integration is producing inconsistent or low-quality outputs and the model choice is not the problem.
Structured output (JSON mode or explicit schema instruction) reduces parsing errors by 90% — always specify the exact output format for programmatic use.
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