Adaline

Adaline is the single platform for product and engineering teams to iterate, evaluate, deploy, and monitor LLMs.

https://adaline.ai

Pillars

Adaline’s core concepts consist of four pillars:

• Iterate

• Evaluate

• Deploy

• Monitor

The platform brings together the entire prompt engineering lifecycle into a single, collaborative and cohesive platform. This allows product and engineering teams to move faster, collaborate more effectively, and deliver better results.

Iterate

Build, test, and refine your prompts within a powerful collaborative editor and playground.

• Dynamic prompting: Use variables in your prompts to store recurrent information, like the personas a model should use to respond.

• LLM playground: Run your prompt across top LLM providers in a safe sandbox.

• Playground history: View your prompt changes across playground runs with rollback options.

Evaluate

Validate your prompts’ performance with test cases at scale.

• Evaluators: Choose evaluators like LLM-as-a-judge, text matcher, JavaScript, cost, token usage, and more.

• Linked dataset: Store and organize thousands of evaluation test cases in datasets.

• Analytics: View detailed evaluation reports with scores, cost, token usage, latency, and more.

Deploy

Ship your prompts to production with confidence.

• Version control: View and manage your prompt changes across versioned deployments.

• Deployment environments: Deploy to isolated environments for safe CI/CD releases.

• Cross-environment control: Promote and rollback deployments between environments.

Monitor

Monitor your AI app using telemetry and continuous evaluations.

• Observability: View, search and filter on real-time and historical traces and spans.

• Continuous evaluation: Monitor prompt performance using continuous evaluations running on live telemetry data.

• Analytics: View curated time-series charts of latency, token usage, cost, evaluation scores, and more.