Fixing Complex Calculation Template Prompts: Streamlining Our Workflow

As we continue to refine our workflows, one challenge has consistently stood in our way—our complex calculation template prompts. Despite our best efforts, we've found that the prompts we’ve developed are too big, too convoluted, and too difficult for us to execute efficiently. The result? Confusion, wasted time, and incomplete outputs. In this blog, we'll walk you through the problem, our plan to fix it, and how breaking down these prompts will streamline our processes and save us valuable time.

The Problem: Overly Complex Prompts

When we first developed our calculation templates, we tried to pack everything into a single, massive prompt. The idea was simple: use one prompt to handle everything. But as we quickly learned, this approach created more problems than it solved.

Too Much Information in One Go The main issue with our original prompts was that they contained too much information. By trying to process everything at once, the model struggled to maintain focus and context. In addition, the overwhelming amount of instructions confused us, leading to incomplete or inaccurate results. A long-winded prompt left us questioning if we were following the correct steps or if something was being overlooked.

Too Many Tasks in One Prompt Another challenge was attempting to complete too many tasks at once. Whether it was extracting values, performing calculations, or verifying results, the prompts required multiple steps in one go. This made it difficult to break down the process into manageable chunks, and as a result, accuracy suffered. The model wasn’t able to prioritize tasks, and neither were we.

The Solution: Breaking It Down Into Manageable Steps

We’ve learned that simplicity is key. Instead of one giant prompt, we’re now focusing on breaking down the task into smaller, clear steps. This approach will allow both us and the model to focus on one thing at a time, ensuring clarity, accuracy, and a smoother execution.

Chunking the Process Our new approach involves chunking the process. We’ll start by extracting the necessary input variables, followed by validating those variables. Then, we’ll move on to the calculations, step-by-step, and finally, we’ll verify the result. Each stage is clear, manageable, and focused.

For example, instead of asking the model to “process the entire template,” we now have prompts like:

• “Extract the variables from the document.”

• “Validate the input values.”

• “Perform the calculation based on the extracted variables.”

• “Verify the result against predefined checks.”

This method ensures that the model can process each task in isolation, reducing the chance of missing key details or losing track of context.

Iterative Prompts and Feedback Loops In addition to chunking the process, we’re also implementing feedback loops. After each step, we’ll confirm that everything is accurate before moving on to the next phase. This allows us to catch errors early and avoid compounding mistakes. By verifying results as we go, we’re ensuring that the output is both accurate and reliable.

The Expected Outcome: A Streamlined Workflow

With these changes, we anticipate several key benefits:

1. Increased Clarity: Breaking the task into smaller steps removes ambiguity and ensures that each part of the process is clear and actionable.

2. Better Accuracy: With each step being verified, we reduce the chance of errors and improve the overall reliability of the results.

3. Faster Execution: Rather than spending time sorting through a complex prompt, we can focus on one step at a time, improving efficiency and reducing confusion.

4. Improved User Experience: The simpler, more structured prompts make it easier for everyone on the team to execute calculations without getting overwhelmed.

Conclusion: Embracing a Simpler, More Efficient Approach

By simplifying our prompts and breaking them down into manageable steps, we’re setting ourselves up for success. This shift will help us stay on track, improve accuracy, and save time—ultimately leading to better outcomes and more efficient workflows.

As we continue to improve our processes, we’ll share updates on our progress. If you’re also working with complex prompts and workflows, we’d love to hear your experiences and any tips you have for simplifying the process.

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