How can prompt tuning be leveraged to increase accuracy in generative tasks like code completion or bug fixing

Question

Can i know How can prompt tuning be leveraged to increase accuracy in generative tasks like code completion or bug fixing?

Answer 1

Prompt tuning can improve accuracy in generative tasks like code completion or bug fixing by optimizing structured prompts with in-context learning, task-specific examples, and reinforcement tuning for more precise model outputs.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• In-Context Learning (ICL) – Provides structured examples to improve model understanding and accuracy.
• Error-Aware Prompts – Defines the issue explicitly before generating the correct solution.
• Reinforcement with Task-Specific Constraints – Encourages precise, high-quality bug fixes.
• Low-Temperature Setting – Uses temperature=0.3 to reduce randomness and enforce accuracy.
• Scalability – Can be extended with few-shot learning and reinforcement learning from human feedback (RLHF).

Hence, prompt tuning with structured examples, in-context learning, and reinforcement-based refinements significantly increases the accuracy of generative tasks like code completion and bug fixing.