Prompt Engineering
Prompt engineering is the practice of designing the instructions and context given to an AI model so that it produces accurate, relevant, and consistent results.
AI
Explanation
A prompt can include the task, background information, constraints, examples, and the expected output format. Common techniques include:
• stating the goal and audience clearly,
• providing relevant context and source material,
• giving examples of good output,
• asking the model to reason step by step,
• defining a structured output format, such as a table or JSON,
• specifying what the model should do when information is missing.
In agentic AI, prompts become system instructions that define an agent's role, tools, permissions, and rules of behavior.
Why It Matters
In finance and SAP use cases, precise prompts reduce errors and make results more reproducible. For example, asking an AI to evaluate a contract change works better when the prompt includes the applicable accounting policy, the contract data, and the required output structure. Well-designed prompts are part of a controlled AI process, not a replacement for review.
Related Terms
Large Language Model · Generative AI · Hallucination · Retrieval-Augmented Generation
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