LLM Development Framework

An LLM Development Framework is an AI software development framework that enables systematic creation, deployment, and management of LLM-based applications and LLM-powered systems.

• Context:
  • It can (typically) provide LLM development tools through:
    • LLM debugging systems for trace logging and error diagnosis.
    • LLM testing frameworks for automated evaluation and performance assessment.
    • LLM monitoring tools for behavior tracking and anomaly detection.
    • LLM dataset management for training data and evaluation sets.
  • It can (typically) support LLM application development through:
    • Prompt engineering tools for template management and version control.
    • Context handling systems for input processing and response generation.
    • Retrieval augmentation for knowledge integration and data access.
    • Model integration interfaces for provider connection and API management.
  • ...
  • It can (often) enable LLM deployment workflows through:
    • CI/CD pipeline integration for automated testing and deployment.
    • Resource management systems for scaling control and cost optimization.
    • Performance monitoring for latency tracking and quality assessment.
    • Security controls for access management and compliance adherence.
  • It can (often) facilitate team collaboration through:
    • Chain trace sharing for workflow visibility.
    • Prompt versioning for iterative improvement.
    • Human feedback collection for model refinement.
  • ...
  • It can range from being a Basic Development Kit to being an Enterprise Development Platform, depending on its feature scope.
  • It can range from being a Local Development Tool to being a Production Framework, depending on its deployment capability.
  • It can range from being a Single Purpose Framework to being a Full Stack Platform, depending on its integration scope.
  • It can range from being an Open Source Framework to being a Commercial Solution, depending on its licensing model.
  • ...
• Examples:
  • Full-Stack LLM Development Framework, such as:
    • ...
  • LLM Orchestration Frameworks, such as:
    • LangChain Framework (2022) for workflow orchestration and component integration.
    • LlamaIndex Framework (2022) for data connection and context augmentation.
    • Haystack Framework (2020) for modular pipeline construction.
  • LLM Performance Frameworks, such as:
    • vLLM Framework (2023) for inference optimization and continuous batching.
    • TensorFlow Extended (2020) for production pipeline management.
  • LLM Development Platforms, such as:
    • Hugging Face Platform (2016) for model sharing and collaborative development.
    • PyTorch Framework (2016) for deep learning development.
  • LLM Agent Frameworks, such as:
    • AutoGen Framework (2023) for multi-agent orchestration.
    • Chainlit Framework (2023) for chat interface development.
  • ...
• Counter-Examples:
  • Traditional ML Frameworks, which lack LLM-specific features and specialized workflows.
  • Basic Model APIs, which provide only inference capabilitys without development support.
  • General Development Frameworks, which lack LLM optimizations and specialized tools.
• See: LLM Application Development, AI Development Framework, Machine Learning Operations Platform, LLM Infrastructure System.

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