Boost LLM Accuracy with Self-Correcting Agent Loops
TL;DR: Single-shot LLM prompts often output broken HTML or buggy layouts. By wrapping the LLM in an agentic loop—where it writes code, renders it to an image, inspects its own work, and edits it iteratively—I found I could achieve highly accurate outputs within five to ten turns.
Recently, I was working on a task where I needed an LLM to generate HTML on the fly. I started out using a standard one-shot approach: write a prompt, ask for the HTML, and try to render the output. But the results were always slightly off. The code looked mostly correct, but when I actually tried to render it, there were always small, annoying layout mistakes.
That is when I decided to move away from basic prompting and wrap the LLM call inside a custom agentic loop. Letting the model inspect its own output changed everything.
How do I make LLM code generation more accurate?
You can make LLM code generation much more accurate by wrapping the model in an agentic loop rather than relying on a single-shot prompt. Giving the LLM tools to execute its own code and inspect the output allows it to self-correct and fix bugs before delivering the final result.
Expecting an LLM to output perfect code on the first run is like asking a developer to write a complex UI with their monitor turned off. We do not write code that way. We write a few lines, refresh the browser, find a weird margin issue, and tweak the code.
By building a loop where the model can execute its code, see what it did wrong, and edit its work, the output quality gets significantly better. It shifts the burden of QA from you to the model itself.
What is an agentic loop and how does it work?
An agentic loop is a design pattern where an LLM repeatedly executes a task, evaluates its own output using external tools, and refines the code over several turns. For visual tasks, this involves generating code, rendering it to an image, and sending that image back to the LLM for inspection.
In my HTML generation experiment, I built a loop with specific tools. Instead of just getting raw text back, the system followed a step-by-step pipeline to let the model review its own visual work.
| Step | Tool / Component | Action |
|---|---|---|
| 1. Write | Write HTML Tool | The LLM generates the initial raw HTML structure. |
| 2. Render | Render HTML Tool | Converts the HTML into a static JPEG image. |
| 3. Inspect | Visual Feedback | The LLM receives the JPEG to inspect the actual visual layout. |
| 4. Edit | Edit HTML Tool | The LLM identifies visual discrepancies and edits the code to fix them. |
This workflow runs continuously. If the LLM spots an issue in the JPEG, it uses the edit tool to adjust the HTML, renders it again, and repeats the process until the output matches the requirements.
Why is visual feedback necessary for code generation?
Visual feedback is necessary because an LLM cannot easily detect layout bugs, overlapping text, or rendering issues from raw code strings alone. Converting the output into an image allows a multimodal model to visually inspect the actual layout and catch mistakes that are invisible in text.
When I only fed the raw HTML string back into the model, it couldn't see the overlap issues. The tags were technically valid, so the text-based model assumed everything was fine.
But when I converted the HTML into a JPEG and sent that image back to the LLM, the model could immediately spot the bugs. Within five to ten turns, the output went from "mostly broken" to exactly what I wanted. Giving the LLM a way to see its own work makes self-correction incredibly effective.
FAQ
How many turns does an agentic loop require to get good results?
In my experience, the loop usually gets things right within five to ten turns. The first couple of turns set up the basic structure, while the remaining turns clean up minor alignment, font issues, and rendering quirks.
Does running an agentic loop increase API latency and costs?
Yes, running multiple iterations increases both latency and token usage. However, for tasks where accuracy is the priority—like generating customer-facing UI components—the trade-off is almost always worth it.
What tools are needed to build a visual self-correction loop?
You need a basic script to orchestrate the loop, a headless browser tool (like Playwright or Puppeteer) to convert HTML to an image, and a multimodal LLM that can accept both text prompts and image inputs.
