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Welcome to Queryloop

Building effective AI applications with Large Language Models (LLMs) requires careful optimization and fine-tuning. Queryloop streamlines this process by automatically identifying the optimal parameters for your language model applications, helping you achieve the precise responses you need for your use case.

Why Queryloop?​

Developing LLM applications involves numerous decisions about model selection, parameter tuning, and deployment strategies. Queryloop simplifies this complexity by providing:

  • Automated parameter identification that takes the guesswork out of optimization
  • Comprehensive experiment tracking to help you understand what works best
  • Sophisticated retrieval and reranking capabilities for enhanced accuracy
  • Flexible deployment options that adapt to your needs

Core Features​

Automatic Parameter Optimization​

Queryloop analyzes your requirements and automatically identifies the best parameters for your application, saving you time and improving accuracy. The platform tests various combinations to find the optimal configuration for your specific use case.

Retrieval Augmented Generation (RAG)​

Our platform excels at combining information retrieval with language generation. This means your LLM applications can leverage external knowledge bases while maintaining accuracy and relevance in their responses.

Comprehensive Experiment Tracking​

Monitor and compare different model configurations through our intuitive dashboard. Understanding how different parameters affect performance helps you make informed decisions about your AI applications.

Flexible Deployment​

Whether you need to deploy through our platform or integrate via API, Queryloop supports your preferred deployment strategy with enterprise-grade reliability.

Getting Started​

Begin your journey with Queryloop by following these steps:

  1. Create your account and set up your organization
  2. Learn about our key features and capabilities
  3. Explore use cases and practical applications
  4. Start building your first LLM application

How This Documentation Is Organized​

Our documentation is structured to support both newcomers and experienced practitioners:

  • Getting Started: Essential guides for new users
  • Features: Detailed explanations of Queryloop's capabilities
  • Use Cases: Real-world applications and examples
  • RAG Series: In-depth exploration of Retrieval Augmented Generation
  • Evaluation: Methods for assessing and optimizing performance

Support and Community​

Join our growing community of AI developers and practitioners:

Need help? Our support team is ready to assist you in making the most of Queryloop's capabilities.

Let's begin building more effective, efficient, and accurate LLM applications together with Queryloop.