# December 2024 ## December 2024 Release Notes ### Enhanced Prompt Engineering Experience #### Prompt Node on Canvas We're excited to announce significant enhancements to Lleverage that improve workflow creation and prompt engineering capabilities, while expanding our model offerings. Here's what's new: * Inline Configuration: Configure your LLM settings, prompts, and variables directly within the workflow canvas * Compare Feature: Test different prompt variations side-by-side to identify the best performing version * Real-time Preview: See prompt results instantly as you make changes, making iteration faster and more efficient #### AI-Powered Prompt Generation We're introducing an intelligent prompt generation feature that helps you create production-ready prompts quickly: * Natural Language Instructions: Generate sophisticated prompts by simply describing your requirements in plain language * Best Practices Built-in: Generated prompts automatically incorporate prompt engineering best practices * Customizable Output: Fine-tune generated prompts to match your specific needs while maintaining their optimized structure ### New Model Support #### OpenAI GPT-4 Optimized (O1) Models We've added support for OpenAI's latest O1 model series: * Enhanced Performance: Access improved response quality and consistency * Optimized Speed: Benefit from faster inference times while maintaining high-quality outputs These updates significantly enhance your ability to build and optimize AI workflows in Lleverage. The new prompt engineering capabilities make it easier to create effective prompts, while the addition of O1 models provides more options for balancing performance, speed, and cost in your applications. We look forward to seeing how you leverage these new capabilities to build even more powerful and efficient AI solutions. *The Lleverage Team* Posted: December 6, 2025\ Version: 0.6.0 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.lleverage.ai/support/release-notes/december-2024.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.