Prompt engineering tactics to stabilize LLM use in backend/data workflows

Prompt engineering tactics to stabilize LLM use in backend/data workflows

A practical guide outlines how to craft precise, context-rich prompts (roles, constraints, examples) and iterate to improve LLM outputs. It highlights that models have different strengths (e.g., Claude for reasoning/ethics, Gemini for multimodal) and links better prompts to fewer hallucinations and lower API spend.