prompt-engineering-patterns

Few-shot prompting improves LLM reliability by using dynamic example selection and embedding-based retrieval to provide diverse demonstrations. This pattern enhances generalization across tasks like classification and extraction by grounding the model with relevant context.

The best way to enforce structured JSON outputs is by applying schema-based patterns that demand type-safe responses. This structured output approach ensures reliable parsing and prevents formatting errors during downstream processing.

Chain-of-thought prompting improves correctness by structuring reasoning patterns with optional self-verification. This mechanism transparently breaks down complex generation tasks, allowing you to audit the model's logic step-by-step.

Numpy is required for this Skill's prompt evaluation and A/B testing workflows. It provides the underlying computational support needed to calculate performance metrics and iteratively refine prompt strategies.

Use progressive disclosure and template systems when scaling modular prompts that load knowledge on demand. This approach supports role-based prompts and guardrails, preventing context overflow and maintaining efficiency in production.

Your prompt workflow lacks auditability because it likely misses structured testing and guardrails. You can fix this by applying iterative refinement, performance metrics, and structured output patterns to enable robust and auditable prompt workflows.