Optimize ai prompts with the 4-d method

[Code-Eat-Rabbit]

Describe your changes:

Fixes : Implement Lyra AI Prompt Optimizer

I implemented the Lyra AI Prompt Optimizer, a Python application that transforms vague user prompts into precision-crafted prompts. This was done by strictly adhering to the specified 4-D methodology (Deconstruct, Diagnose, Develop, Deliver), incorporating platform-specific optimizations, and supporting both basic and detailed optimization modes. The implementation includes the core optimization logic (lyra_prompt_optimizer.py), a comprehensive test suite (test_lyra.py), an interactive demo (demo_lyra.py), and detailed documentation (README_LYRA.md and LYRA_SUMMARY.md).

Type of change:

• Bug fix
• Improvement
• New feature
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation

Checklist:

• I have read the CONTRIBUTING document.
• My PR title is Fixes <issue-number>: <short explanation>
• I have commented on my code, particularly in hard-to-understand areas.
• For JSON Schema changes: I updated the migration scripts or explained why it is not needed.

New feature

• The issue properly describes why the new feature is needed, what's the goal, and how we are building it. Any discussion
  or decision-making process is reflected in the issue.
• I have updated the documentation.
• I have added tests around the new logic.

---

 

[cursor]

Cursor Agent can help with this pull request. Just @cursor in comments and I'll start working on changes in this branch.
_{Learn more about Cursor Agents}

[github-actions]

Hi there 👋 Thanks for your contribution!

The OpenMetadata team will review the PR shortly! Once it has been labeled as safe to test, the CI workflows
will start executing and we'll be able to make sure everything is working as expected.

Let us know if you need any help!