Vibe Coding - Conversational Software Development - Part 4 Guiding AI Through Iteration

Inroduction

Welcome to the fourth and final post in my Vibe Code series. In the previous article, I explained how system prompts can steer AI behaviour by setting initial expectations and boundaries. But if you have worked on even any mildly complex application, you will know that first draft is never the final version. You always have some sort of UX tweaks, performance enhancements, or new feature requirements.

That is where task-level prompting comes into the play. Once your foundation is in place, the next step is to provide instructions to AI to build, improve, and polish step by step. In this blog post, I will share several approaches that have worked well for me.

Task-level prompt

Begin Small: Simplicity First, Structure Later

Shipping into production is never a one-and-done task. There are always iterations, refactoring, testing, and including non-functional requirements like accessibility to account for. It is unrealistic to expecting an AI assistant to accomplish this all in a single prompt.

Instead, start small and iterate. In the early phases, simplicity wins. Here is an example of an intentionally open-ended prompt I might use.

Create a dashboard with spaces for visual elements. Assume type of charts or some reason you think works best for this data.

Here I am just focussed on the dashboard screen. Using such prompt will direct AI without boxing it. You are setting a vibe and offering intent rather than detailed instructions. The prompt is open and allow AI to make suggestions. But you have a long way to go from here.

Application Development Iterations

One Task at a Time: Iterating with Focus

Let us go deeper, take the example from my previous blog around London Air Quality Data. Once the initial dashboard created, I will narrow the focus.

Consider this London Air Quality Data and analyse the dataset. Suggest data visualization or graph that captures key trends on the dashboard.

Now the AI is not just coding, it is thinking. Below are a few suggestions it came up with.

London Air Quality Data Insights

It might surface insights you hadn’t noticed or suggest charts that suit the story your data is telling. This is where AI becomes a true collaborator, not just a code generator.

If London Air Quality Data is not available at above link, I have committed a copy of it, as CSV, at the time writing this post.

Refining with Follow-Up Prompts

Here’s a dashboard Copilot generated for me after some initial prompts. It worked, but visually it still felt basic and lacks the depth I was after.

Initial Design

So, I refined with these two targeted prompts:

Prompt 1

Visualize air quality trends over time. Aggregate data by half-year, use smoother lines, add subtle gridlines, and stretch the graph to full page width.”

Prompt 2

The dashboard has a white background and basic styling. Can you apply a modern dark theme and make it more visually appealing?

Here is the result. A cleaner and more elegant dashboard that is much closer to production quality.

Final Dashboard

You can push it further based on your requirements:

• Make the chart mobile friendly.

• Add a tooltip to show percentage change from the previous half-year.

• Do you notice any unusual patterns we should highlight?

This is where you can make AI to act as your pair programmer, iterating with you.

Structure the Conversation for Better Outcomes

If you are not sure where to begin? Try out different prompts but they keys is to keep them focused, open-ended. Try something like:

Give me a few different dashboard layouts that summarise the data.

First results are rarely the final one. Don’t stress about getting everything perfect upfront. It usually takes three or four iteration and sometimes more to before it is ready for production. The goal is to progress.

Prompting Pitfalls

Task level prompting is not an art rather it needs logical structured approach to guide AI assistants. Below are a few DOs and DON’T’s

Don’ts

• Mix multiple objectives (bug fixes, feature requests and styling) in one prompt
• Leave AI to guess your requirements
• Assuming AI will automatically meet your compliance, security and coding standards

Instead

• Focus on one goal at a time
• Clearly specify your expected outcome
• Encourage creativity from AI like what could be most insightful chart for this data set?