Okareo

Okareo is a special tool made to help evaluate and fix issues with AI agents, especially those that use big language models (LLMs). It has advanced tools to spot errors, prevent mistakes, and keep things accurate when used in real-world settings. Okareo makes the process simple, so developers can focus on creating great things instead of spending too much time on fixing issues. It is really helpful for managing LLMs in real use, helping users fine-tune their systems to work well in specific areas and making sure the results are accurate and reliable.

Benefits

Okareo offers several key benefits for developers working with LLMs. It has automated testing to ensure accurate and reliable results. This feature can be part of the development process, making it very useful. Okareo also lets users define custom evaluation metrics for their specific needs. This includes standard metrics like consistency, conciseness, relevance, and BLEU score, as well as more practical measures.

Another big benefit is scenario-based testing. Okareo allows the creation of different scenarios to thoroughly test model robustness. These scenarios can cover edge cases, rephrasing, conditionals, misspellings, and more. By making synthetic data, Okareo helps evaluate the model''s performance against many inputs, ensuring it is ready for real-world use.

Okareo also provides detailed error analysis for applications running in real-time, including RAGs and agentic networks. This feature helps pinpoint issues with clear explanations, track them, and offer guidance for resolution. It gives a full view of the errors and their solutions.

Use Cases

Okareo is great for developers and teams working with LLMs in real-world settings. It can be part of continuous integration and continuous deployment (CI/CD) pipelines, allowing for automated testing and evaluation of LLMs as part of the development workflow. This ensures that any changes to the LLM or the application code are thoroughly tested before use, keeping the LLM outputs reliable and accurate.

The platform''s fine-tuning and optimization features are very helpful for adapting base models to specific uses. The Fine-Tuning Co-Pilot feature automates the workflow for making datasets and checking performance, ensuring that the models work best for their specific tasks. This improves their performance and reliability.

Okareo''s advanced monitoring tools help spot errors, drift, and potential mistakes in live settings. This keeps the LLM outputs accurate and reliable even when the model is in use. The platform gives real-time insights into the model''s performance, allowing for quick identification and resolution of issues.

Additional Information

Okareo''s synthetic data generation feature creates synthetic scenarios organized by feature and expected model behaviors. Each scenario comes with baseline metrics, giving a full tool for evaluating the model''s performance against many inputs. This is very useful for making data samples for new AI-powered experiences, ensuring that the model is tested against real scenarios.

Using Okareo''s advanced features and functions, developers can make sure their LLM-powered applications are reliable, accurate, and perform well. The platform''s full approach to LLM evaluation and testing makes it a valuable tool for anyone working with big language models.