Feature

Generate Scenarios for LLM Evaluation and Fine-Tuning

With built-in generators in Okareo, no additional tools is needed to create specific scenarios for your needs

Feature

Generate Scenarios for LLM Evaluation and Fine-Tuning

With built-in generators in Okareo, no additional tools is needed to create specific scenarios for your needs

Feature

Generate Scenarios for LLM Evaluation and Fine-Tuning

With built-in generators in Okareo, no additional tools is needed to create specific scenarios for your needs

Need reliability and robustness in your LLM apps?

Build with confidence with Okareo

Okareo automatically generates synthetic scenarios for your specific use cases to give you better coverage for LLM evaluations.

Okareo synthetic scenarios is for all phases of development: describing expected behavior, prototyping, testing, evaluating, and fine-tuning

Establishing Baselines

Having baselines and constantly evaluating your metric progress is the only way your LLM app will ever see the light of production. Using a few ad-hoc input samples doesn't represent reality or build confidence. Getting representative data samples, in particular for new AI-powered experiences, is nearly impossible. Synthetic data addresses this problem and is an important tool in the AI engineering toolkit. Okareo creates synthetic scenarios organized by feature and expected model behaviors. Each scenario is paired with baseline metrics.

Okareo creates synthetic scenarios organized by feature and expected model behaviors. Each scenario is paired with baseline metrics.

Establishing Baselines

Okareo creates synthetic scenarios organized by feature and expected model behaviors. Each scenario is paired with baseline metrics.

Synthetic Data Loop

Establishing repeatable evaluations in dev pipelines (CI/CD) is a high ROI activity. Evaluations require sufficient data and clear signals to guide improvement decisions based on metrics movement.

Okareo synthetic scenarios allow you to expand visibility into model behaviors and, coupled with evaluations, makes these decisions more data-driven.

Synthetic Data Loop

Okareo synthetic scenarios allow you to expand visibility into model behaviors and, coupled with evaluations, makes these decisions more data-driven.

Pre-Production vs. Production

Production will introduce new, unforeseen scenarios. There are no guarantees that the LLM outputs that were ‘passing’ before will continue to do so. Evaluations can be used offline against production data to identify new failure types and online to determine if the LLM output is of sufficient quality for use. You can easily expand coverage by using Okareo to generate new scenarios to include new production failure modes.

Production will introduce new, unforeseen scenarios. There are no guarantees that the LLM outputs that were ‘passing’ before will continue to do so. You can easily expand coverage by using Okareo to generate new scenarios to include new production failure modes.