Prompt Engineering

Design a system prompt by layering identity, task definition, constraints, and output formatting. This structures LLM instructions to ensure predictable outputs and reliable parsing for downstream features.

Chain-of-thought (CoT) prompting is a technique that implements step-by-step reasoning within LLM prompts. You should use it for complex tasks where guiding the model through intermediate logical steps improves accuracy.

Format LLM outputs as valid JSON by applying strict output formatting constraints within the system prompt. This enforces specific structural boundaries, generating responses ready for reliable programmatic parsing.

Select effective few-shot examples by choosing instances that clearly demonstrate the desired LLM behavior and output structure. Crafting these examples within the prompt guides the model to reproduce the correct pattern for new inputs.

The best way to manage prompt versioning is to establish a strategy for tracking and managing prompt evolution. This involves documenting changes to instruction structure, few-shot examples, and constraints to maintain consistent LLM performance.

You need to define task instructions precisely to prevent ambiguous LLM responses and ensure optimal performance. Explicitly guiding users through defining tasks eliminates vagueness, allowing the model to execute the specific operation accurately.