Dynamic Prompting

Features

Dataset integration

Overview

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

Adaline

Adaline Documentation

Introduction

Adaline Platform Documentation

Pillars provides dedicated workflow to craft, test, share, and observe your prompts.

Pillars

A creative workspace within Adaline where you build and refine your prompts.

The Evaluate pillar is your testing powerhouse within Adaline, where you validate and optimize your prompts using real data.

The Deploy focus mode is your production control center within Adaline, where tested prompts transition into live applications.

The Monitor focus mode is your production intelligence center within Adaline, where you track and analyze every prompt interaction in real-time.

Best Practices

Glossary

Dynamic Workers

The `ConfigType` is a configuration type used for defining prompt-specific settings and provider configurations in Adaline. It provides a flexible way to specify LLM provider details, model selection, and runtime settings for prompt execution.

Config

The `MessageType` is the fundamental building block for communication with LLM providers in Adaline. It represents both messages sent to and received from language models, with a rich type system that supports various content modalities.

Message

The `ToolType` is the fundamental type for defining tools and functions that can be called by LLM providers in Adaline. It provides a structured way to define function schemas that language models can use for tool calling capabilities.

Tool

The Deployments API allows you to retrieve deployed prompts from your prompt. This API provides two main operations: retrieving a specific deployment by ID and retrieving the latest deployment for a given environment.

Get Deployments

Retrieve the latest deployment for a specific prompt in a given deployment environment.

Get Latest Deployments

Create an individual log span that can be associated with an existing trace.

Create Log Span

Create a log trace with optional spans. This endpoint allows you to create a complete trace with multiple spans in a single request.

Create Log Trace

The Update Log Trace API allows you to modify existing log traces in your LLM applications on Adaline. This endpoint enables you to update trace properties such as status, end time, attributes, and tags after the trace has been created.

Update Log Trace API Reference

Adaline is the single platform for AI teams, empowering them to iterate, evaluate, deploy, and monitor. In our collaborative environment, both technical and non-technical members unite to move faster, think clearer, and create better. 

Unite technical and non-technical teams on one platform. Build, test, and deploy AI prompts with enterprise-grade collaboration tools. Try Adaline free.

Adaline: The Platform That Helps AI Teams Ship 30% Faster

Adaline: AI Team Platform for Prompt Management | Iterate, Evaluate, Deploy

The single platform for prompt iteration, evaluation, deployment & monitoring. Join leading AI teams using Adaline to ship products 30% faster.

Adaline - Where AI Teams Build Better, Faster

This is a quick-start guide to setting up your Adaline account, connecting a model, writing prompts, testing them, and deploying them.

For Product Leaders

For Engineers

Adaline's core concepts consist of four pillars that we also call “Focus Modes”: Iterate, Evaluate, Deploy, and Monitor. These focus modes allows the users to isolate their workflow but also to switch between them with ease. Pillars provides dedicated workflow to craft, test, share, and observe your prompts. 

The Iterate focus mode is your creative workspace within Adaline, where you build and refine your AI prompts. This is the foundation of your prompt engineering journey, providing all the tools you need to craft, test, and perfect your prompts before moving them through evaluation and deployment.

Intuitive workspace, you can write, tune, and iterate your prompts

Compose prompt using a structured interface.

Messages

Tools allow you to extend the capabilities of the LLMs by invoking services.

Tools

Choose LLM and customize how your LLM responds to your Prompts.

Model and Parameters

Control how your AI responds by defining its output format.

Response Schema

Run your prompts and see the response in real-time.

Test how your LLM interacts with tools before building real integrations

Tool Calling

Execute, restart, continue, or stop your prompts with different run options

Run Prompt

Track and revisit all your previous playground runs.

Run History

Edit and manage your prompt variables effortlessly. 

Simple and Flexible Input for Your Prompts

Variables Values

Connect and manage all your data sources for prompt testing and deployment.

Linked Dataset

The Evaluate focus mode is your testing powerhouse within Adaline, where you validate and optimize your prompts using real data. This is where you transform assumptions into insights by running systematic evaluations that measure performance, accuracy, and reliability. Connect your prompts to datasets, apply evaluation methods, and get actionable metrics to refine your AI solutions.

Test and optimize your prompts with data-driven insights.

Use LLMs to evaluate your prompt responses..

LLM-as-a-Judge

Write custom JavaScript code to evaluate prompt responses.

Javascript

Validate your prompt responses with precise text pattern matching and keyword detection.

Text Matcher

Track the financial impact of your prompts with accurate cost measurement.

Cost

Test the length of your prompt responses with flexible measurement options.

Response Length

Latency

Store and organize your prompt test cases.

Setup for Evaluations

Create datasets for in-playground testing.

Setup for Playground

Set up your datasets with bulk test cases.

Import CSV

Execute your evaluator tests and get instant insights on prompt performance.

Runs

Access and analyze your evaluation reports to understand prompt performance.

View

Restore your editor and playground to the exact state from any evaluation run.

Rollbacks

The Deploy focus mode is your production control center within Adaline, where tested prompts transition into live applications. This is where you manage release cycles, configure environments, and ensure smooth rollouts with confidence. Deploy gives you the infrastructure to ship prompts to production while maintaining version control and the ability to rollback instantly if needed.

Adding Environments

Delete an environment and all associated deployments 

Deleting Environment

Deploy from Editor

Deploy a prompt from one environment to another.

Deploy from Environment

Roll back to any previous prompt version instantly.

Rollback

The Monitor focus mode is your production intelligence center within Adaline, where you track and analyze every prompt interaction in real-time. This is where data meets decisions, providing deep insights into performance, usage patterns, and costs. Monitor transforms raw metrics into actionable intelligence that keeps your AI solutions running optimally.

Telemetry is your real-time performance dashboard in Adaline, capturing every detail of how your prompts perform in production. It provides the data foundation for understanding usage patterns, identifying optimization opportunities, and ensuring consistent quality. Think of telemetry as your AI solution's vital signs monitor, keeping you informed of health and performance around the clock.

Analytics

Logs

Continuous Evaluations

Observability provides deep insight into your AI system's internal operations, going beyond surface metrics to reveal how prompts execute in production. It's your microscope for understanding the complete journey of every request, from initial call to final response. With observability, you can diagnose complex issues, optimize performance, and control costs with precision.

Traces

Spans

Prompt Library

Adaline.ai Cookbooks are comprehensive, step-by-step guides that demonstrate how to leverage the Adaline platform—the single environment where AI teams can iterate, evaluate, deploy, and monitor their AI applications. These practical tutorials are designed to help both technical and non-technical team members work together more efficiently throughout the AI development lifecycle.

Adaline Cookbooks for product leaders translate technical AI concepts into strategic business frameworks. These accessible guides help you understand AI capabilities, limitations, and implementation requirements without needing engineering expertise. You'll discover how to effectively scope AI features, set realistic expectations, and create meaningful success metrics that align with business goals. Our cookbooks provide decision-making frameworks for model selection, feature prioritization, and risk management, along with collaboration strategies to bridge communication gaps with technical teams. Whether you're planning your first AI feature or optimizing existing implementations, these cookbooks will help you navigate the product decisions needed to deliver AI capabilities that create genuine user value.

Adaline Cookbooks provide comprehensive technical guides for implementing production-ready AI systems. These detailed references contain code samples, architectural patterns, and technical best practices for each stage of the AI development lifecycle. As an AI engineer, you'll find step-by-step instructions for solving complex technical challenges, from prompt engineering to deployment architecture. Our cookbooks cover advanced topics like custom evaluators, multi-node agent systems, and monitoring frameworks, with practical examples you can adapt to your specific use cases. Each cookbook is designed to help you leverage Adaline's four pillars—Iterate, Evaluate, Deploy, and Monitor—to build more robust, efficient, and reliable AI applications.

Configure and manage API credentials for AI model providers at the workspace level.

Get started

Iterate

Evaluate

Deploy

Monitor

Guides

References

Overview

Features

Dynamic Prompting

Dataset integration

Get started

Iterate

Evaluate

Deploy

Monitor

Guides

References

​Features

​Dynamic Prompting

​Dataset integration

Features

Dynamic Prompting

Dataset integration