What is Adaline?

Adaline offers a comprehensive platform designed for teams developing applications using large language models (LLMs). It provides a collaborative environment facilitating rapid iteration and development cycles. The platform supports teams in saving time and resources by enabling AI-powered testing across extensive datasets, which helps ensure confident deployment through robust logging and continuous testing mechanisms.

Key functionalities include a versatile project interface for prompt engineering, compatible with major LLM providers like OpenAI, Anthropic, and Google Gemini, allowing fine-tuning of model parameters. It features intuitive prompt editing with variable support, automatic version control for easy tracking and restoration, and a playground for experimentation. The platform integrates intelligent evaluations, such as context recall and LLM-as-a-judge rubrics, alongside heuristic checks like latency and content filtering. Debugging tools, production logging, dataset management, and an analytics dashboard further support the development lifecycle, providing insights into performance, usage, and costs.

Features

Collaborative Playground: Iterate on prompts with support for major providers, variables, and automatic versioning.
Intelligent Evaluations: Evaluate prompts using AI-powered tests like context recall and LLM-as-a-judge rubrics.
Heuristic-Based Evaluations: Check criteria such as response latency and specific content filtering.
Prompt Version Control: Automatically saves prompt versions for easy tracking and restoration.
Debugging Tools: Filter evaluation results to quickly identify and address failing tests.
Production Logging & Monitoring: Evaluate production completions against criteria and track usage, latency, and performance metrics via APIs.
Dataset Management: Build datasets from logs, upload CSVs, or edit collaboratively within the workspace.
Analytics Dashboard: Gain insights into inference counts, evaluation scores, costs, and token usage.

Use Cases

Developing and iterating on AI applications using LLMs.
Testing and evaluating prompt performance for reliability.
Collaborating on prompt engineering within development teams.
Monitoring LLM performance and usage in production environments.
Ensuring AI model outputs meet specific criteria like context recall or latency.
Building and managing datasets for AI model regression testing.
Debugging AI model responses based on structured evaluations.