prompt-engineering-patterns

To improve LLM prompt quality, you need structured prompt design that incorporates few-shot examples, chain-of-thought reasoning, and validation steps. This reduces errors and stabilizes model behavior for production tasks.

The best way to structure few-shot examples for LLM tasks is to carefully select and format edge cases within your prompt. This scaffolding guides the model's classification and extraction behavior, ensuring consistent and controllable outputs.

Chain of thought prompting works by guiding the LLM through sequential reasoning and verification steps before generating a final answer. This pattern, combined with self-consistency checks, stabilizes outputs for harder problem-solving tasks.

Yes, you can use prompt optimization workflows for a production customer support assistant by building a stable system prompt, adding edge case examples, and tightening output formats so responses are easier to parse and trust.

You need structured outputs in your LLM prompts to make responses easier to parse and trust. By applying constraints and validation steps, you gain controllability over the model behavior and reduce parsing errors in downstream applications.

To track and fix LLM prompt failures during iteration, apply an optimization workflow that uses A/B testing, metrics tracking, and failure analysis. This iterative refinement process reduces errors and stabilizes model behavior over time.