Prompt Engineering for Accounting Applications

Accuracy is critical in accounting, where even a small error can lead to costly mistakes or compliance issues. To reduce the risk of AI-generated inaccuracies or “hallucinations,” include specific instructions in your prompt that anchor the AI to known facts or sources. For example, you might add directives like: “Base your response only on the data provided below,” or “If the answer isn’t in the attached text, do not supplement it with outside information.”

You can also instruct the AI not to fabricate information and to explicitly say if it’s unsure. Another effective technique is to have the AI cite its sources or reference specific documents (if you’ve provided excerpts or if the system has retrieval capabilities). For instance: “Provide ASC references for each accounting treatment you mention,” or “Include a footnote with the IRS publication number for any tax rules cited.” This approach forces the AI to stick to verifiable facts and makes it easier for you to check the response against authoritative materials.

In addition, consider asking the AI to explain its reasoning or show its work. Prompting it with “Show your calculation steps” or “Explain why you arrived at that conclusion, step by step,” can help expose any leaps in logic. You can even request the AI to “provide direct citations to sources and a brief explanation of your reasoning”. This way, you not only get an answer but also insight into how the AI got there, which you can then validate. If something looks off in the explanation or the sources seem unrelated, that’s a red flag that the response may not be trustworthy. As one expert put it, nobody wants a black-box answer; if an intern gave you a conclusion, you’d ask them to “prove it,” and you should do the same with AI.

However, even with these precautions, it’s essential to keep the human in the loop. AI outputs should always be received with a degree of skepticism, as no model is infallible. For high-stakes tasks, consider running the same prompt through multiple AI models or verifying outputs against trusted sources. Treat AI responses like the first draft of a junior staffer’s work – valuable, but in need of careful review before final use. Keeping the human in the loop is essential for maintaining professional standards and ensuring that critical decisions are based on accurate, contextually sound information.

Prompt engineering is an iterative process. Rarely will a complex query yield a perfect answer in one try, especially when dealing with nuanced accounting tasks. Treat each AI interaction as part of a back-and-forth conversation, where follow-up prompts are expected and necessary for refining the output.

For example, if your initial prompt yields an overly general response, try adding more context or asking the AI to focus on a specific aspect of the task. You might start with, “Summarize this client’s financial statements,” and follow up with, “Focus specifically on liquidity ratios and recent cash flow trends.” This iterative approach helps guide the AI toward more precise and actionable insights.

Additionally, if the AI’s response is too lengthy, overly technical, or missing key details, don’t hesitate to refine the prompt with clearer instructions. Simple follow-ups like, “Make this more concise,” or “Rephrase for a non-technical audience,” can dramatically improve the quality and relevance of the output. Treat the process like a collaborative dialogue, where each response is a stepping stone toward the final, polished result.

Modern AI platforms often include features that can help optimize prompts and improve the quality of responses. For example, ChatGPT offers a “Custom Instructions” feature that allows users to set persistent context, like your role and typical tasks, which the AI will consider in every response. This can be especially useful for accountants who want the AI to consistently adopt a particular tone or perspective, such as a tax expert or CFO.

Additionally, many AI tools allow you to upload documents or provide structured data, which can dramatically improve the quality of outputs. For example, if you’re asking the AI to analyze a financial statement, uploading the actual file instead of pasting raw text can provide more precise context and lead to better insights. Similarly, using file attachments in tools like Microsoft 365 Copilot or Google’s Gemini can reduce ambiguity and improve response accuracy by providing direct access to the relevant data.

Finally, keep in mind that many AI tools reset their context between sessions. If you’re working on a long project, consider saving effective prompts for reuse or creating a shared prompt library for your team. This practice can streamline workflows and ensure consistency across multiple projects.

Creating a prompt library, which is a collection of proven prompt templates, can help your team complete recurring accounting tasks with greater speed, accuracy, and consistency. Whether it is summarizing audit findings, drafting disclosures, or reviewing contracts, having go-to prompts ensures that less experienced staff start with a solid foundation instead of guessing. These templates can save time, reduce mistakes, and improve overall quality by capturing best practices refined by more senior team members.

To build your library, start with a shared document or internal wiki organized by task type or tool. Encourage your team to contribute prompts that have worked well, along with tips or variations. As AI tools and workflows evolve, treat the library as a living resource that gets updated, tested, and improved over time. This simple habit can turn prompt engineering from an individual skill into a firm-wide asset.