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Introduction:
Contentable.ai is an end-to-end testing platform for generative AI. It enables users to A/B test multiple AI models, compare performance metrics like speed, cost, and output quality, and build and deploy low-code AI models. The platform supports major AI vendors and streamlines rapid prototyping and team collaboration.
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Contentable.ai Product Information
What is Contentable.ai?
Contentable.ai is an end-to-end testing platform for generative AI. It allows users to A/B test multiple AI models, compare speed, costs, and outputs, and build and deploy low-code AI models. It supports major AI vendors and facilitates rapid prototyping and collaboration on AI models.
How to use Contentable.ai?
Users can compare models side-by-side, test prompts, design workflows, and share models with their team. The platform enables prompt management across multiple vendors and provides shareable links for users to access models and provide feedback.
Contentable.ai's Core Features
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A/B testing of multiple AI models
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Side-by-side comparison of model outputs
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Low-code AI model building and deployment
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Prompt management across multiple vendors
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Visual workflow design
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Collaboration features for teams
Contentable.ai Use Cases
#1
Comparing the performance of different AI models from OpenAI, Google, and Llama.
#2
Testing different prompts to optimize AI model outputs.
#3
Designing complex AI workflows without coding.
#4
Sharing AI models with users for real-world application and feedback.
FAQ from Contentable.ai
How many LLM providers are supported by Contentable? +
Contentable currently supports OpenAI, Google, and Llama models, with plans to add more providers in the future.
How can I test different prompts in the playground? +
You can test different prompts by disabling the sync prompt button. This allows you to use two distinct prompts and evaluate their outputs across various scenarios.
Can I finetune my model? +
A fine-tuning feature is currently in development and will be available soon.
How many models can I compare side by side in the playground? +
You can compare up to three models side-by-side and run up to three scenarios simultaneously.
How can I save a model and share it with my users? +
Once you have finalized your prompt in the playground, click the save icon and enter a name and description. The model will appear in the models tab. Click the '...' button and select 'View shareable link' to share the model with users, allowing them to use it and provide feedback.
Can I compare the chat functionality from multiple models? +
Yes. Simply toggle the mode in the top-left corner from text to chat. In chat mode, you can compare chat outputs from various providers based on your system prompt.
Can I share the playground view with my team members? +
You can share the playground with team members at any time by clicking the share button in the top-right corner. Team members can execute the same prompt to verify results. Real-time collaboration features, including commenting and notifications, are currently in development.
Contentable.ai Pricing
Related Model Comparison Pages
Use these comparison pages to understand the trade-offs between the models most relevant to Contentable.ai.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
Compare Gemini 1.5 Pro Vision Deprecated and Gemini 1.0 Pro Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus long-context workloads.
Compare Gemini 1.0 Pro Vision Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus general-purpose AI workloads.
Compare Gemini 1.5 Pro Vision Deprecated and Gemini 1.0 Pro Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus general-purpose AI workloads.
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