The Challenge of Prompt Context Clutter
When refactoring code using LLMs, merging instructions, legacy source code, and target parameters into a single unstructured prompt causes models to lose track of rules or modify code they shouldn’t touch.
Case Study: Lost Logic in Legacy Conversions
A migration team used an AI model to refactor legacy Java classes to Kotlin. The model converted the code but omitted key transaction management logic, introducing security bugs into production.
The Bug: Context Confusion
The prompt mixed instructions and source code directly: “Refactor this Java code to Kotlin. Make sure it uses modern framework features. Here is the code…”
Under the hood, the model confused structural variables in the code with formatting instructions.
The Fix: XML Structure Isolation
We updated the prompt template to wrap each context element in clear XML tags. This helps LLM parsing layers segment instructions from source code:
<system_instructions>
You are a software engineer converting Java to Kotlin.
Rule: Do not alter security or transaction annotations.
</system_instructions>
<source_code>
// Paste Java code here
</source_code>
<target_requirements>
Convert to Kotlin using standard coroutine scopes.
</target_requirements>This clear isolation improved structural translation accuracy from 64% to 99% across complex files.