Table of Contents
How Can You Improve AI Prompt Results by Setting Goals, Defining Context, and Iterating?
Master the best practices for writing AI prompts to ensure good results. This guide for finance professionals explains how setting a clear goal, defining context, specifying format, and iterating on your prompts can dramatically improve AI outputs while avoiding common pitfalls.
Question
What are good practices to ensure that your AI prompts lead to good results?
A. Setting a clear goal
B. Defining the context and audience
C. Specifying format and tone
D. Including technical jargon
E. Iterating and refining the result
F. Uploading proprietary company-specific data
G. Keeping the prompt as short and generic as possible
H. Using prompts no longer than 50 characters
Answer
A. Setting a clear goal
B. Defining the context and audience
C. Specifying format and tone
E. Iterating and refining the result
Explanation
The selected answers represent the core principles of effective prompt engineering, a skill focused on providing clear, contextual, and iterative instructions to guide a Generative AI model toward a high-quality outcome.
Good Practices for Effective Prompting
A. Setting a clear goal: This is the most crucial step. You must know what you want the AI to accomplish before you write the prompt. A well-defined objective prevents ambiguous or irrelevant responses. For example, instead of “Tell me about interest rates,” a clear goal would be “Explain the likely impact of a 50-basis-point interest rate hike on the tech sector for a client presentation.”
B. Defining the context and audience: Providing context gives the AI the necessary background to frame its response accurately. Specifying the audience dictates the tone, complexity, and vocabulary. An explanation of “beta” for a high school student is very different from an explanation for a CFA charterholder.
C. Specifying format and tone: This practice structures the AI’s output for immediate usability. You can request information in a bulleted list, a markdown table, JSON format, or a formal paragraph. Similarly, defining the tone (e.g., professional, persuasive, neutral) ensures the response aligns with its intended use.
E. Iterating and refining the result: The first prompt rarely produces a perfect result. Effective prompting is a conversational process. You should treat the AI’s initial response as a draft, then provide follow-up prompts to correct, expand, or reformat it until it meets your requirements.
Practices to Avoid
D. Including technical jargon: Unless the goal is to work within a specific technical domain, excessive jargon can confuse the model or lead to unnecessarily complex outputs. The primary goal is clarity, which is usually best achieved with plain language.
F. Uploading proprietary company-specific data: This is a critical security and compliance risk, not a best practice. Publicly available AI tools process data on their servers, and uploading sensitive client information or internal financial data can lead to data breaches and regulatory violations. This practice should be strictly avoided unless using a secure, private enterprise version of an AI platform with explicit data privacy guarantees.
G. Keeping the prompt as short and generic as possible: This is the opposite of a good practice. Short, vague prompts yield generic, unhelpful, and often inaccurate results. The more specific and detailed the prompt, the better the AI can understand the user’s intent and deliver a relevant response.
H. Using prompts no longer than 50 characters: This is an arbitrary and counterproductive limitation. While prompts should be concise, complex tasks require detailed instructions that will naturally exceed this length. The prompt’s length should be determined by the need for clarity and completeness, not a character count.
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