This white paper by Mphasis explains prompt engineering as crafting precise, context-rich instructions to guide AI for accurate, relevant outputs. It covers LLM basics, settings like temperature and top-K, and the role of linguistic analysis. Techniques include zero-, one-, few-shot prompting, chain-of-thought, and image prompting. Best practices stress clarity, examples, inclusivity, iteration, and scope control. It warns of risks like AI hallucination and debunks myths, emphasizing continuous optimization. Effective prompt engineering improves model accuracy, reliability, and applicability across domains while ensuring ethical, bias-aware use.

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