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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.
Explore each pillar’s overview in the tabs below.
Iterate
Evaluate
Deploy
Monitor
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.
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.
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 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.
