Orq.ai's Video Library

Welcome to Orq.ai, your control center for building, testing, and deploying LLM-powered software.

In this webinar, Kyra Dresen (orq.ai) walks through a practical, end-to-end approach to AI model evaluation using orq.ai's Experiments module - from planning your success metrics to running live experiment comparisons in the UI.

In this webinar, Kyra Dresen (orq.ai) walks through a practical, end-to-end approach to AI model evaluation using orq.ai's Experiments module - from planning your success metrics to running live experiment comparisons in the UI.

This step-by-step tutorial walks you through the complete Orq.ai platform. You'll learn how to manage prompts, run experiments, set up evaluations, deploy to production, build Agents and monitor then using Traces.

𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿𝗾 is a Python evaluation framework designed for running multiple experiments in parallel and measure AI performance directly from your Python code.

𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗼𝗿𝗲𝘀 are persistent storage for agent memories, allowing your agents to maintain context and recall information across conversations Check out a short build to learn when to use Memory Stores and when to go for a Knowledge Base.

Learn how to build your first agent in Agent Studio

Learn how to set up Orq.ai MCP Server in your CLI

Find out how to leverage human feedback to improve system performance

Learn how to use historical data to build curated datasets in Orq.ai.

Learn how to easily import test data and use it as part of an evaluation workflow in Orq.ai.

Find out how you can create a knowledge base in Orq.ai in less than 2 minutes.

Learn how to enable models from OpenAI, Anthropic, and more for your GenAI use case.

Learn how to set up your first deployment and publish prompt and model changes.

Learn how to use our platform to iterate safely on prompt configurations.

Discover how to do A/B tests, carry out canary releases, and do contextualized routing.

Use logs and traces in to gain full visibility into your LLM app’s behavior.

Discover how to compare AI models, configure prompts, and evaluate results.

Discover how to interpret outputs from experiment to derive actionable insights.

Find out how to add a second model to an existing experiment.

Learn how to measure LLM-generated output using Orq.ai.

Learn how Tidalflow delivers LLM-based features using Orq.ai.