Trainkore is a prompting and RAG (Retrieval-Augmented Generation) platform designed to automate prompts and save costs. It offers features like auto prompt generation, model switching, evaluation, observability, and a prompt playground. It integrates with various AI providers and frameworks like Langchain and LlamaIndex.
Trainkore
Trainkore is a prompting and RAG platform for automating prompts and saving costs.
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What is Trainkore?
How to use
Trainkore can be used by importing the Trainkore library in Node.js, Python, or using Curl. You can then create chat prompts, manage models, and observe metrics through the platform's interface.
Core Features
- Automated Prompt Generation
- Model Switching
- Observability Suite
- Prompt Evaluation
- Prompt Versioning
- Model Router
Use Cases
- Dynamically generate prompts for various use cases.
- Select the cost-optimized model for prompt execution.
- Debug prompts with detailed logs and metrics.
FAQ
What AI providers does Trainkore work with?
Trainkore works with OpenAI, Gemini, Coherence, Anthropic, Azure, and many more AI providers.
What frameworks does Trainkore integrate with?
Trainkore natively integrates with Langchain, LlamaIndex, and more.
What are the key features of Trainkore?
Key features include auto prompt generation, model switching, observability suite, prompt evaluation, and prompt versioning.
Pricing
Pros & Cons
Pros
- Cost savings through auto prompt generation and model switching.
- Improved performance with model router.
- Enhanced observability and debugging capabilities.
- Integration with popular AI frameworks.
- Prompt versioning for better management.
Cons
- May require some coding knowledge to integrate (Node.js, Python, Curl).
- Coming Soon features are not yet available.
- The extent of supported models beyond the famous ones is unclear.