Mastering the Agentic Workflow: Enhancing LLM Problem Solving

Introduction to Agentic Workflows

When it comes to leveraging Large Language Models (LLMs), there's often a common path we all follow: starting small with a basic model and gradually moving up the chain to more complex ones, all in the hopes of hitting that sweet spot of effectiveness. But what happens when you’ve maxed out your options and are still grappling with challenges? The real game-changer here is applying an agentic workflow. Instead of pinning all your hopes on a single prompt for a single LLM, you break it down into manageable parts.

The Challenge Encountered

So, where did I find myself recently? I was knee-deep in what seemed like a straightforward problem involving an LLM—arguably one of my go-to tools. The issue arose while I was processing grocery order data and juggling two key elements: a list of items not included in a customer’s order and notes from an employee on the missing items. The challenge? To connect the missing items in Column B with adequate explanations in Column A. Any item that lacked a reasonable justification needed to be flagged for review in a text file—a format that looked something like: Cheese - No explanation.

Initial Attempts and Limitations

Now, here’s where things got a bit murky. I managed to get reasonable outputs, but there were those pesky edge cases that complicated my progress. Take cheese, for instance. If the explanation amounted to simply meh, that just didn’t cut it as a valid reasoning. Sure, I could identify the explanations, but discerning their validity was where I hit a wall. It became clear to me: relying on a single prompt directed at the largest LLM just wasn’t going to work.

Implementing the Agentic Workflow

To tackle my predicament, I opted for an agentic workflow, systematically breaking down the entire problem into a sequence of prompts. Here’s how that process unfolded:

• Prompt 1: Extract items from Column B lacking valid explanations.
• Prompt 2: Assess the validity of the extracted explanations.
• Prompt 3: Compare Prompt 2 results back to Column B.
• Prompt 4: Format the output suitably.

Understanding the Workflow Breakdown

As I reviewed the workflow, it became clear how distinct each prompt functioned. Take Prompt 1: its main job was to extract data. In contrast, Prompt 2 acted as a classification tool. As for the third prompt? Its role was a comparison mechanism, while the fourth focused on data generation.

Insights Gained

At the heart of the issue was the realization that trying to cram all these functions into one prompt simply overwhelmed the LLM, creating confusion. By breaking these tasks into smaller chunks, I simplified the entire process, ultimately leading to a much more successful outcome.

A Practical Example to Illustrate

Let’s dive deeper into the process with a hypothetical scenario:

• First prompt might yield: Ham: Old, Cheese: May.
• Second prompt evaluates: The term 'meh' is inadequate.
• Third prompt runs the comparison, flagging that cheese is missing a solid justification.
• Final prompt sends back: “Cheese - No valid explanation found.”

Conclusion: Breaking Down Tasks for Success

In conclusion, even the most advanced LLM doesn’t ensure success with overly complex queries tossed its way all at once. Sometimes, the smartest approach to problem-solving calls for a multi-layered strategy. This method—what we refer to as an agentic workflow—helps simplify complex challenges by breaking them down into manageable pieces. By segmenting your tasks, you can truly harness the potential of LLMs and achieve those sought-after results.This approach is further detailed in our guide on agentic workflows.