LLM-Centric System Architecture

A LLM-Centric System Architecture is an AI-centric system architecture for LLM-centric systems with supporting systems (to enable natural language processing tasks).

• AKA: Language Model System Architecture, LLM-Based System Architecture, AI-Language System Architecture, Large Language Model System Design.
• Context:
  • It can (typically) process Natural Language Input through LLM-centric processing systems.
  • It can (typically) manage System Context through LLM-centric state management systems.
  • It can (typically) handle System Integration through LLM-centric interface systems.
  • It can (typically) coordinate Task Processing through LLM-centric orchestration systems.
  • It can (typically) maintain System State through LLM-centric memory systems.
  • It can (typically) ensure Data Security through LLM-centric security systems.
  • It can (typically) optimize System Performance through LLM-centric optimization systems.
  • It can (typically) manage Resource Usage through LLM-centric resource management systems.
  • ...
  • It can (often) implement Knowledge Access through LLM-centric retrieval systems.
  • It can (often) provide Response Generation through LLM-centric generation systems.
  • It can (often) support Error Handling through LLM-centric validation systems.
  • It can (often) manage Resource Allocation through LLM-centric scheduling systems.
  • It can (often) handle Request Routing through LLM-centric routing systems.
  • It can (often) support System Monitoring through LLM-centric observability systems.
  • It can (often) maintain Data Privacy through LLM-centric privacy systems.
  • It can (often) enable System Scalability through LLM-centric scaling systems.
  • ...
  • It can range from being a Basic LLM-based Processing System Architecture to being an Advanced LLM-based Reasoning System Architecture, depending on its reasoning capability.
  • It can range from being a Simple LLM-based System Architecture to being a Complex LLM-based System Architecture, depending on its system complexity.
  • It can range from being a Stateless LLM-based System Architecture to being a Stateful LLM-based System Architecture, depending on its state management capability.
  • It can range from being a Simple LLM-based Integration Architecture to being a Complex LLM-based Integration Architecture, depending on its integration complexity.
  • It can range from being a Synchronous LLM-based Architecture to being an Asynchronous LLM-based Architecture, depending on its processing model.
  • It can range from being a Single Region LLM-based Architecture to being a Multi Region LLM-based Architecture, depending on its deployment scope.
  • It can range from being a Development LLM-based Architecture to being a Production LLM-based Architecture, depending on its deployment stage.
  • ...
  • It can integrate with Knowledge Base System for LLM-centric information access.
  • It can connect to Vector Database System for LLM-centric semantic search.
  • It can support Monitoring System for LLM-centric performance tracking.
  • It can utilize Cache System for LLM-centric response optimization.
  • It can leverage Security System for LLM-centric access control.
  • It can employ Load Balancer System for LLM-centric traffic distribution.
  • It can implement Logging System for LLM-centric audit trail.
  • It can use Analytics System for LLM-centric usage analysis.
  • It can incorporate Backup System for LLM-centric disaster recovery.
  • It can deploy Failover System for LLM-centric high availability.
  • ...
• Examples:
  • Request Processing LLM Architectures for direct language model interaction, such as:
    • Synchronous Processing Architectures for real-time LLM interaction, such as:
      • Direct API Processing Architecture for single request handling.
      • Streaming Response Architecture for continuous output processing.
    • Asynchronous Processing Architectures for high-volume LLM tasks, such as:
      • Batch Processing Architecture for document processing pipeline.
      • Queue-Based Processing Architecture for parallel request handling.
  • Task Orchestration LLM Architectures for complex language processing workflows, such as:
    • Agent-based Orchestration Architectures for autonomous task execution, such as:
      • Self-Directing Agent Architecture for task planning sequence.
      • Multi-Agent Collaboration Architecture for distributed task processing.
    • Tool Integration Architectures for external capability access, such as:
      • Managed Plugin Architecture for controlled tool access.
      • Dynamic Tool Router Architecture for capability discovery.
  • Knowledge Integration LLM Architectures for information-aware processing, such as:
    • Retrieval Augmented Architectures for context-enhanced generation, such as:
      • Semantic Search Architecture for relevance-based retrieval.
      • Hybrid Search Architecture for multi-modal information retrieval.
    • Knowledge Base Architectures for structured information access, such as:
      • Vector Store Architecture for embedding-based lookup.
      • Graph Database Architecture for relationship traversal.
  • Deployment LLM Architectures for production system implementation, such as:
    • Cloud Native Architectures for scalable LLM deployment, such as:
      • Serverless Function Architecture for stateless processing.
      • Container Orchestration Architecture for distributed deployment.
    • Edge Computing Architectures for distributed LLM processing, such as:
      • Local Inference Architecture for on-device processing.
      • Edge-Cloud Hybrid Architecture for distributed computation.
  • Specialized Domain LLM Architectures for industry-specific applications, such as:
    • Healthcare Processing Architectures for medical information handling, such as:
      • Clinical Documentation Architecture for medical record processing.
      • Research Analysis Architecture for literature processing.
    • Legal Processing Architectures for legal information handling, such as:
      • Case Analysis Architecture for precedent matching.
      • Document Review Architecture for legal extraction.
  • Safety-Enhanced LLM Architectures for controlled model deployment, such as:
    • Ethical Processing Architectures for responsible model usage, such as:
      • Bias Detection Architecture for fairness monitoring.
      • Content Filtering Architecture for safety enforcement.
    • Compliance Architectures for regulated environments, such as:
      • Privacy-Preserving Architecture for data protection.
      • Audit Trail Architecture for activity tracking.
  • Research LLM Architectures for model development, such as:
    • Training Pipeline Architectures for model customization, such as:
      • Parameter-Efficient Training Architecture for model adaptation.
      • Feedback Integration Architecture for model improvement.
    • Evaluation Architectures for model assessment, such as:
      • Benchmark Testing Architecture for performance measurement.
      • Quality Assurance Architecture for capability verification.
  • Experimental LLM Architectures for advanced capability research, such as:
    • Multi-Modal Architectures for cross-domain processing, such as:
      • Vision-Language Architecture for image-text processing.
      • Speech-Text Architecture for audio-text conversion.
    • Advanced Processing Architectures for novel computation, such as:
      • Quantum-Enhanced Architecture for hybrid computation.
      • Neuromorphic Architecture for brain-inspired processing.
  • Layer-based LLM-Centric System Architectures (layer-based system architecture), with:
    • LLM-Centric Semantic Interface Layer for user-LLM interaction management.
    • LLM-Centric Orchestration Layer for LLM workflow coordination.
    • LLM-Centric Language Processing Layer for core LLM operations.
    • LLM-Centric Foundation Layer for LLM infrastructure services.
  • ...
• Counter-Examples:
  • Traditional Rule-Based System Architecture, which lacks LLM-centric natural language capability.
  • Simple API Gateway System Architecture, which lacks LLM-centric processing capability.
  • Basic Web Service System Architecture, which lacks LLM-centric context awareness.
  • Static Content System Architecture, which lacks LLM-centric dynamic response.
  • Traditional Database System Architecture, which lacks LLM-centric semantic understanding.
  • Monolithic System Architecture, which lacks LLM-centric service isolation.
  • Basic Caching System Architecture, which lacks LLM-centric response optimization.
  • Simple Load Balancing Architecture, which lacks LLM-centric request routing.
• See: AI System Architecture, Natural Language Processing System Architecture, System Prompt Engineering Architecture, Context Management System Architecture, API Integration System Architecture, Distributed System Architecture, Cloud System Architecture, Scalable System Architecture, Resilient System Architecture.

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References

2025

• LLM
  • Layered Intelligence Integration: The architecture must incorporate multiple layers of AI processing capabilities, from basic text processing to advanced reasoning, with each layer building upon the previous one's outputs while maintaining clear boundaries for maintainability and scaling.
  • Context Management Framework: A sophisticated state management system must be implemented to handle conversation history, user preferences, and system state across multiple interactions, enabling coherent and contextually-aware responses across sessions.
  • Dynamic Prompt Engineering System: The architecture should include a flexible prompt management system that can dynamically construct, optimize, and adapt prompts based on user input, context, and desired outcomes, supporting both template-based and programmatically generated prompts.
  • Semantic Processing Pipeline: A robust information processing pipeline for handling natural language inputs must be established, incorporating embedding services, vector stores, and semantic search capabilities to enable sophisticated information retrieval and processing.
  • Multi-Modal Integration Capability: The system should be designed to handle various types of inputs and outputs beyond text, including images, structured data, and potentially audio/video, with appropriate processing pipelines for each modality.
  • Distributed Cache Architecture: Implementation of a sophisticated caching strategy at multiple levels – from response caching to semantic caching and embedding caching – to optimize performance and reduce unnecessary LLM calls.
  • Observability and Monitoring Framework: Comprehensive monitoring of LLM operations, including token usage, response latency, quality metrics, and cost tracking, with detailed logging and analytics capabilities for system optimization.
  • Scalable Knowledge Integration: The architecture must support dynamic integration with various knowledge sources, including vector databases, traditional relational databases, and document stores, with efficient retrieval and update mechanisms.
  • Security and Governance Layer: Implementation of robust security measures including prompt injection prevention, PII detection, data sanitization, and access control, along with comprehensive audit logging and compliance monitoring.
  • Vendor Abstraction Layer: A flexible abstraction layer for LLM providers that allows easy switching between different models and vendors while maintaining consistent interfaces and handling provider-specific optimizations transparently.

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