Prompt Engineering implementation checklist

Verify that the model output consistently adheres to a strict JSON or XML schema using tools like Zod, Pydantic, or model-native structured output modes.

Include at least 3-5 diverse input/output pairs in the prompt to demonstrate handling of edge cases and specific formatting requirements.

Explicitly list forbidden elements (e.g., 'Do not include conversational filler', 'Do not mention your training data') to prevent unwanted verbosity.

Use clear delimiters like triple backticks (```) or XML tags to separate system instructions, context, and user-provided input.

Implement a retry mechanism or a secondary prompt to fix malformed JSON strings when the initial generation fails validation.

For complex tasks, instruct the model to 'think step-by-step' and verify that the reasoning path leads to the correct conclusion.

Break prompts requiring more than three logical steps into a chain of separate, smaller LLM calls to reduce reasoning errors.

Ensure the most critical information is placed at the very beginning or end of the prompt to mitigate 'lost in the middle' retrieval issues.

Include a step where the model reviews its own output for logical inconsistencies before returning the final response.

Provide a 'ground truth' document and instruct the model to only use provided facts, citing specific sections to reduce hallucinations.

Test the prompt against common injection attacks like 'Ignore previous instructions' or 'System override' to ensure instruction persistence.

Implement regex or NLP-based pre-processing to scrub personally identifiable information from user inputs before they reach the LLM.

Enable and configure provider-specific safety settings (e.g., OpenAI Moderation API) to block harmful or inappropriate content generation.

Treat the system prompt as code; ensure it is stored in version control and not directly exposed to end-users via client-side code.

Set hard limits on user input character counts to prevent denial-of-service style attacks via massive token consumption.

Calculate the average token count for system instructions and few-shot examples to estimate cost per 1,000 requests.

Remove redundant adjectives and filler words from the prompt to reduce input token costs without impacting output quality.

Verify if the task can be performed by a lower-cost model (e.g., GPT-4o-mini vs GPT-4o) and document any performance degradation.

Measure the Time to First Token and total generation time across 100 samples to ensure it meets application SLA requirements.

Implement semantic or exact-match caching for frequent queries to reduce API costs and improve response times.

Maintain a versioned set of 50+ diverse inputs and their expected 'perfect' outputs for automated testing.

Use a more capable model (e.g., Claude 3.5 Sonnet) to grade the performance of the production model based on a rubric.

Assign semantic versions to prompt templates and track which version was used for every production inference call.

Run the new prompt version alongside the current baseline for 5% of traffic to compare success metrics (e.g., conversion, error rate).

Test the prompt across at least two different model providers (e.g., OpenAI and Anthropic) to ensure portability and identify provider-specific biases.