Workflow Tips

You've explored the mindset, the foundational tools, and a powerful toolkit of prompting techniques. The final piece of the puzzle is putting it all together in a structured, professional workflow.

Having a set of techniques is one thing; knowing how to use them systematically to solve problems is what separates an amateur from a professional prompt engineer. This page provides a practical workflow tips for developing, debugging, and documenting your prompts in a team environment.

Iterative Process 🔄

The most important thing to remember is that prompt engineering is a cycle, not a straight line. Your first attempt will rarely be perfect. The professional workflow is a loop of crafting, testing, and refining until you achieve the desired output with consistent reliability.

Inadequate prompts can lead to ambiguous or inaccurate responses, hindering the model's ability to provide meaningful output. The goal of this workflow is to move from an initial, inadequate prompt to a refined, production-ready one as efficiently as possible.

Debugging Prompts 🐞

When your prompt doesn't produce the output you expect, don't just try random changes. Work through a systematic checklist to diagnose the problem.

• Simplify and Be Direct: Is your language overly complex or your instruction vague? Reword your prompt to be as concise and clear as possible. Start your task with a strong verb (e.g., "Generate," "Classify," "Summarize") to remove ambiguity.
• Be Specific About the Output: If the model's output is unstructured or the wrong style, you likely haven't been specific enough. Explicitly instruct the model on your desired format (e.g., "Return the output as a numbered list," "Use valid JSON"), length ("Summarize in a single paragraph"), and style ("Write in a formal, academic tone").
• Provide Examples (Upgrade Your Technique): This is often the most powerful debugging step. If a zero-shot prompt is failing because the task is too nuanced, upgrade it to a few-shot prompt. Showing the model 3-5 high-quality examples of what you want is the clearest possible way to communicate your intent.
• Check Your Examples: If you're using a few-shot prompt and it's still failing, the error might be in your examples. Review them carefully for typos, formatting errors, or logical inconsistencies.

Document Everything ✍️

In a professional environment, your work needs to be reproducible, understandable, and maintainable. You must document your prompt engineering attempts in a structured way. This allows you to track what went well, learn from what didn't, and collaborate effectively with your team.

We recommend creating a shared document (like a Google Sheet) to log your key prompt versions. This log is invaluable for debugging, testing prompts against new model versions, and providing a complete record of your work.

A Template for Documenting Prompts

Field	Description
Name	A unique name and version for your prompt (e.g., customer_feedback_classifier_v3).
Goal	A one-sentence explanation of what this prompt attempt is trying to achieve.
Model	The name and version of the LLM you are using (e.g., gemini-pro).
Temperature	The temperature setting used (e.g., 0.2).
Token Limit	The max output tokens configured.
Top-K	The Top-K setting used.
Top-P	The Top-P setting used.
Prompt	The full, exact text of the prompt.
Output	One or more examples of the output generated by the model.