LLMCoder Modes: Graph. Refinement

[anacarsi]

Combining two code snippets into a single, more optimized solution can sometimes lead to a better overall performance, but it depends on the nature of the task and the specific details of the code snippets. Considerations:

• Algorithmic Improvements: For example, if one snippet uses a more efficient algorithm for a specific part of the task, integrating it with another snippet could lead to improved performance.
• Reducing Redundancy: If the two code snippets perform similar computations or share common steps, combining them can help eliminate redundancy and improve code maintainability. However, we are concerned it maya as well reduce it.
• Parallelization: If the task allows for parallelization, combining code snippets might enable parallel processing.
• Function Inlining: Inlining functions from both snippets into a single code block can sometimes eliminate function call overhead and improve performance.

The llmcoder._step() function is being restructured in order to change the prompt of the feedback loop, so that the LLM will evaluate this aspects before the combination.