prompt-engineering

Fix inconsistent LLM outputs by diagnosing common failure modes like underspecification and context rot, then applying the smallest effective change to your system prompt using explicit success criteria and structured sections.

The best way to structure a prompt for reliable structured outputs is using XML-style sections for context placement and applying few-shot prompting techniques to enforce precise formatting and reduce hallucination risk.

Use few-shot prompting for format-sensitive outputs, self-consistency for correctness, and tree-of-thought reasoning techniques for exploration, selecting the pattern based on your specific task type and reliability requirements.

Task drift and sycophancy in agentic workflows occur due to instruction attenuation and context rot, fixable by rewriting agent prompts into clearer contracts with explicit constraints and token-efficient context handling.

Debug prompt failure modes by identifying specific issues like underspecification or instruction attenuation, then generating clearer prompt contracts with appropriate role definitions, format constraints, and technique-specific guidance.

Improve token efficiency and context handling for format-sensitive outputs by designing prompts with structured sections, optimizing context placement strategy, and applying technique-specific guidance for better caching.