Prompt Engineering
Prompt engineering is the practice of designing inputs to large-language models that reliably elicit accurate, well-formatted, and task-appropriate outputs, encompassing zero-shot, few-shot, chain-of-thought, and system-prompt strategies. As a hard skill it requires understanding how LLMs respond to instruction phrasing, role specification, output format constraints, and example demonstrations, as well as iteratively testing and refining prompts to reduce hallucination and improve consistency. Battery engineers, scientists, and analysts apply prompt engineering to get reliable results from AI assistants for code generation, literature synthesis, report drafting, data interpretation, and technical Q&A within automated pipelines.
