Popular Prompt Engineering Techniques

1. Improves Accuracy and Relevance

Different prompting techniques help reduce errors and hallucinations by guiding AI to produce more precise and contextually appropriate answers. For example, Chain of Thought (CoT) prompting enhances logical reasoning, while self-consistency prompting ensures more reliable responses.

2. Enhances Control Over AI Output

By using instruction-based prompting, users can control the format, depth, and tone of AI responses. This is useful for structuring financial reports, tax summaries, or compliance documentation.

3. Optimizes AI for Different Use Cases

• Zero-shot prompting is useful for general knowledge and quick responses.

• Few-shot prompting helps AI recognize patterns for structured outputs.

• Role-based prompting ensures AI responses align with industry-specific expertise.
  Understanding when to use each technique makes AI more effective across different tasks.

4. Saves Time and Reduces Iterations

The right prompting technique minimizes the need for multiple corrections. For example, interactive prompting allows users to refine responses efficiently in a conversation, while CoT prompting ensures the AI reasons through complex problems correctly in one go.

5. Improves AI Adaptability and Customization

Different prompting techniques allow AI to adapt to different user needs—whether generating financial insights, explaining tax laws, or summarizing accounting regulations in a simple manner. Persona-based prompting ensures responses match the user’s preferred tone and style.

6. Strengthens AI Reliability in Decision-Making

In fields like accounting, tax research, and financial analysis, AI-generated insights must be reliable. Self-consistency prompting helps ensure AI’s responses remain logical and trustworthy across multiple answers.

7. Helps Overcome AI Limitations

AI models have weaknesses, such as hallucinations and biases. Using instruction-based and CoT prompting, users can help AI reason more clearly and avoid misleading outputs.

In conclusion, mastering various prompting techniques allows users to tailor AI responses, improve accuracy, save time, and enhance reliability in professional tasks like accounting, tax research, and financial reporting. The more effectively you prompt AI, the more valuable and efficient it becomes in your workflow.

Shot-based prompting techniques involve guiding an AI model using varying numbers of examples to influence its response.

• Zero-shot prompting provides no examples, relying solely on the model’s pre-trained knowledge. It is useful for general knowledge questions and quick responses but may lack accuracy for complex tasks.

• One-shot prompting provides a single example to establish context and structure, improving response relevance and consistency.

• Few-shot prompting offers multiple examples, helping the AI recognize patterns and generate more accurate, structured, and context-specific responses.

These techniques help tailor AI outputs based on the complexity of the task and the level of control needed over the response. Then, when are the case where zero-shot prompts are more effective than few-shots?

The Cases Where Zero-Shot Prompting is More Useful Than Few-Shot Prompting:

1. General Knowledge Questions – "What is the capital of France?" (No need for examples)

2. Simple Definitions – "Define amortization in accounting."

3. Basic Translations – "Translate 'Good morning' into Spanish."

4. Quick Fact-Checking – "Who is the CEO of Apple?"

5. Summarization of Well-Known Topics – "Summarize the benefits of cloud computing."

6. Ad-Hoc Research Queries – "Explain the latest AI trends in accounting."

7. Generating Variations of a Phrase – "Provide synonyms for 'cost reduction'."

8. Generating Creative Text – "Write a short motivational quote for accountants."

Why Not Use Few-Shot Prompting in These Cases?

1. Few-shot prompting is useful when the AI needs to recognize a pattern from examples, such as following a specific response format or reasoning process.

2. If the question is simple and factual, adding examples won’t improve accuracy significantly and may waste time.

3. Zero-shot is faster and more efficient when there’s no need for complex context or structured learning.

Chain of Thought (CoT) prompting is a technique that encourages the AI to explain its reasoning step by step before giving a final answer. This method helps the model break down complex problems into smaller, logical steps, leading to more accurate and consistent responses. It is especially useful for math problems, multi-step reasoning tasks, and detailed explanations, as it reduces errors by making the model “think out loud” before concluding. 

Here is an example.

Prompt: "Explain concisely and step by step how Schedule M-1 reconciles book income to taxable income when there is tax-exempt interest."

AI responses:

Schedule M-1 reconciles book income to taxable income by adjusting for differences between financial accounting income and tax income. When there is tax-exempt interest, the reconciliation is as follows:

Step 1: Start with Book Income

• Begin with the net income per books (financial accounting income).

Step 2: Add Back Expenses Related to Tax-Exempt Income

• Line 5c: Add back any expenses that were deducted in the financial books but are not deductible for tax purposes because they relate to tax-exempt income (e.g., interest expense on loans used to purchase tax-exempt bonds).

Step 3: Subtract Tax-Exempt Interest

• Line 7: Subtract the tax-exempt interest income because it increases book income but is not included in taxable income.

Step 4: Calculate Taxable Income

• Adjusted book income is calculated by adding and subtracting all necessary items, including the tax-exempt interest and related expenses, resulting in the taxable income.

Example:

1. Book Income: $100,000

2. Tax-Exempt Interest: $10,000 (included in book income but not taxable)

3. Interest Expense Related to Tax-Exempt Bonds: $2,000 (deducted in books but not deductible for tax)

• Add back the $2,000 related expense: $100,000 + $2,000 = $102,000

• Subtract the $10,000 tax-exempt interest: $102,000 - $10,000 = $92,000

Result:

• Taxable Income: $92,000

This process ensures the correct taxable income is reported by adjusting for the non-taxable nature of the interest and the non-deductible nature of related expenses.

Role-based and persona-based prompting both guide AI by assigning a specific identity or style to its responses, but they serve different purposes.

Role-based prompting assigns a specific role or persona to the AI, such as “Act as a tax advisor” or “Explain like a college professor.” This helps the AI tailor its response to fit the expected expertise level, tone, and style of communication. The benefits include more relevant and context-specific answers, enhanced realism in dialogue, and improved engagement, especially when addressing industry-specific topics or explaining complex concepts to different audiences. It makes the output more relatable and easier to understand. 

This technique assigns an expertise or profession to the AI to guide its responses.

1. AI Acting as a Tax Consultant
   Prompt: "You are a tax consultant. Explain how a small business can legally minimize its tax liability."
   AI Response: "As a tax consultant, I recommend maximizing deductions, using tax credits, considering pass-through taxation structures like an S-corp, and deferring income where possible to lower taxable income."

2. AI Acting as an Internal Auditor
   Prompt: "You are an internal auditor. How would you assess a company's financial controls for fraud prevention?"
   AI Response: "As an internal auditor, I would evaluate segregation of duties, perform transaction audits, assess control mechanisms for high-risk areas, and review financial statements for inconsistencies."

3. AI Acting as an Accounting Professor
   Prompt: "You are a college professor. Teach a beginner student about depreciation methods in accounting."
   AI Response: "Great question! Depreciation spreads the cost of an asset over its useful life. There are several methods: straight-line depreciation (equal amounts each year), declining balance (accelerated depreciation), and units of production (based on usage). Let's break down an example together!"

This method directs the AI to respond with a specific tone, style, or personality, such as being humorous, formal, or empathetic. Unlike role-based prompting, which focuses on expertise and context, persona-based prompting influences the voice and manner of communication. This enhances engagement, makes interactions more relatable, and can tailor the user experience to fit different audiences. It’s especially useful for customer support, educational content, and creative writing, where the tone significantly impacts user perception. 

Persona-based prompting tailors AI responses to match a specific tone, style, or personality.

1. Casual Tone for a Beginner Accountant
   Prompt: "Explain tax deductions like you're a friendly mentor guiding a new accountant on their first job."
   AI Response: "Hey there! Tax deductions are like little rewards for businesses—they help reduce taxable income. Think of them as expense write-offs that the IRS allows, such as office supplies, travel costs, and even home office expenses!"

2. Formal and Professional Style
   Prompt: "Explain the concept of deferred tax liabilities in a professional and structured manner."
   AI Response: "Deferred tax liabilities arise when taxable income is lower than accounting income due to temporary timing differences. These liabilities are recorded on the balance sheet and are expected to be settled in future periods when the temporary differences reverse."

3. Humorous and Engaging Style
   Prompt: "Explain cash flow management like you're a stand-up comedian."
   AI Response: "Managing cash flow is like keeping your fridge stocked—you need enough food (cash) to last until payday, but not so much that it spoils (sits idle). You want money moving, but not disappearing into a black hole of impulse spending!"

Role-based prompting is about what the AI knows, while persona-based prompting is about how it communicates. While they can be combined for tasks requiring both expertise and tone, such as "You are a CPA explaining tax deductions in a clear and conversational manner," they remain separate concepts. 

Self-consistency prompting involves asking the AI to generate multiple answers to the same question and then selecting the most consistent or logical one. This approach reduces errors and improves accuracy by leveraging the model’s ability to consider different perspectives before choosing the best response. It’s particularly useful for complex reasoning tasks or when a reliable, well-justified answer is needed. By comparing multiple outputs, self-consistency prompting ensures higher-quality and more credible results. 

Here is an example. In this case, we let the AI select the most logical and complete version out of 3 different responses.   

Prompt: "List three ways a small business can improve cash flow. Generate three different responses to this prompt. Then select the most logical and complete version. "

Response 1

1. Shorten accounts receivable cycles – Send invoices promptly and follow up on overdue payments.

2. Cut unnecessary expenses – Eliminate nonessential subscriptions and reduce energy costs.

3. Negotiate payment terms with suppliers – Extend deadlines to conserve cash longer.
   
   

Response 2

1. Increase sales through promotions – Attract more customers to boost short-term revenue.

2. Lease instead of buy equipment – Lower upfront costs and spread payments over time.

3. Monitor cash flow regularly – Identify trends and take corrective actions early.
   
   

Response 3

1. Encourage early payments – Offer discounts to customers who pay ahead of schedule.

2. Improve inventory management – Avoid overstocking and reduce storage costs.

3. Delay non-urgent purchases – Postpone expenses that are not immediately necessary.
   
   

Selected Response: Response 1

Reason:
Response 1 offers a well-rounded approach, covering:

• Faster inflows (collections),

• Controlled outflows (expenses), and

• Supplier negotiation, a practical lever for timing payments.

It is the most logical and complete because it balances customer-side, internal, and vendor-related strategies, all directly impacting cash flow.

Interactive prompting involves having an ongoing dialogue with the AI, refining the output through multiple follow-up questions. It allows users to clarify ambiguities, request additional details, or adjust the response format. The benefits include improved accuracy, greater customization, and deeper exploration of complex topics. This approach is especially useful for iterative tasks like problem-solving, detailed explanations, or when users need tailored outputs. It enhances the AI’s responsiveness and adaptability to specific needs. 

Here are some examples of interactive prompting.

User: "What is the difference between cash and accrual accounting?"

AI: "Cash accounting recognizes revenue and expenses when cash is received or paid, while accrual accounting records them when earned or incurred."

User: "Which method is better for a small business?"

AI: "Cash accounting is simpler and better for small businesses with minimal inventory, while accrual accounting is preferred for businesses with credit transactions and inventory tracking."

User: "What IRS rules apply to businesses choosing between these methods?"

AI: "The IRS generally allows businesses with under $27 million in revenue to use cash accounting, while larger businesses must use accrual accounting."