Athina AI: Code Execution Flow Visualization for Building and Debugging AI Applications

General Introduction

Athina AI is a collaborative AI development platform designed to help teams rapidly build, test, and monitor AI features. The platform provides a rich set of tools and features, including dataset evaluation, hint management, data annotation, and experiment management.Athina AI supports both technical and non-technical users to work collaboratively, streamlining the AI application development process and enabling teams to move AI applications into production environments faster.

Function List

• Data set evaluation: Evaluate datasets quickly using more than 50 preset assessments or configure custom assessments.
• Cue Management: Iterate quickly on prompts, test different models, compare responses, and manage prompts using built-in version control and deployment features.
• data annotation: Labeling and managing datasets using LLM-driven workflows to support labeling team collaboration.
• Experimental management: Run evaluations in development, CI/CD or production environments to automatically detect and fix regressions.
• observability: Comprehensive monitoring of LLM usage, assessment scores and usage metrics to ensure reliability of AI applications.
• stream management: Build complex pipelines by linking hints, API calls, searches, code functions, and more.
• Self-hosted deployment: Fully deploy Athina in your own VPC to ensure data privacy and security.

Using Help

Installation process

Athina AI supports self-hosted deployment, which allows users to fully deploy Athina in their own VPC to ensure data privacy and security. Below is the installation process:

1. Download Athina: Visit the official Athina website to download the latest version of the Athina installer.
2. Configuration environment: Configure the required environment variables and dependencies according to the documentation provided on the official website.
3. Deployment of Athina: Run the installation package and follow the prompts to complete the deployment process.
4. Access platforms: Once deployed, access the Athina platform through your browser to get started.

Guidelines for use

Data set evaluation

1. Uploading a dataset: Upload the dataset to be assessed on the platform.
2. Selection of assessment criteria: Select preset assessment criteria or configure customized assessment criteria.
3. Operational assessment: Click on the "Run Assessment" button and the platform will automatically assess the dataset and generate an assessment report.

Cue Management

1. Create a Tip: Create new prompts in the Prompt Management module.
2. Test Tips: Select different models, enter prompts, and test the model's response.
3. Comparative response: Compare the responses of different models and choose the best cue.
4. version control: Manage different versions of a prompt using the built-in version control feature.
5. Deployment Tips: Deploy prompts to production environments and monitor the effectiveness of the prompts in real time.

data annotation

1. Creating annotation tasks: Create a new annotation task in the Data Annotation module.
2. task sth.: Assign annotation tasks to annotation team members.
3. Labeling data: Annotation team members annotate data using LLM-driven workflows.
4. Review of labeling results: Review the labeling results to ensure data quality.

Experimental management

1. Create an experiment: Create new experiments in the Experiment Management module.
2. Configuring Experimental Parameters: Configure the parameters and evaluation criteria for the experiment.
3. running experimentClick the "Run Experiment" button, the platform will automatically run the experiment and generate an experiment report.
4. Analyze the results of the experiment: Analyze experimental results to optimize models and cues.

observability

1. Monitoring LLM Usage: View LLM utilization and assessment scores in the Observability module.
2. Setting Alarms: Configure alert rules to monitor the performance of AI applications in real time.
3. View Log: View detailed log messages to see how each step was performed.