Complexity‐Based Routing - joehubert/ai-agent-design-patterns GitHub Wiki

Home::Overview of Patterns

Classification

Efficiency Pattern

Intent

To optimize both cost and performance by intelligently directing simple queries to lightweight models and complex queries to more powerful models based on the complexity of the request.

Also Known As

Model Tiering, Query-Based Model Selection, Adaptive Model Routing, Complexity-Driven Dispatch

Motivation

Large language models vary significantly in capabilities, response times, and operational costs. Using the most powerful (and expensive) model for every request is inefficient and unnecessarily costly, while using only lightweight models limits the system's capabilities for handling complex tasks.

The Complexity-Based Routing pattern addresses this challenge by implementing an intelligent routing mechanism that assesses the complexity of incoming queries and directs them to the most appropriate model. For example:

  • Simple factual questions can be handled by smaller, faster, and less expensive models
  • Complex reasoning tasks, creative writing, or specialized domain questions can be routed to more powerful models
  • Critical or high-stakes queries might always be directed to the most capable models regardless of apparent complexity

This approach enables organizations to balance cost, performance, and capability effectively while maintaining high-quality responses across the entire range of user requests.

Applicability

When to use Complexity-Based Routing:

  • You operate systems with access to multiple LLMs of varying capabilities and costs
  • Your application receives queries with widely varying complexity levels
  • Cost optimization is important but cannot come at the expense of response quality
  • Response time is a critical factor, and simple queries should be processed quickly
  • You need to scale your application efficiently while managing computational resources
  • Your system serves diverse user needs ranging from simple information retrieval to complex problem-solving

Structure

To do...

Components

The key elements participating in the pattern:

  • Complexity Classifier: Evaluates incoming queries to determine their complexity level using heuristics, statistical methods, or a dedicated classification model. May analyze factors such as query length, presence of domain-specific terminology, number of constraints or requirements, and linguistic complexity.

  • Routing Logic: Makes decisions about which model to use based on the complexity classification, potentially considering additional factors like current system load, user preferences, or business rules.

  • Model Tier System: A hierarchy of language models organized by capability, cost, and response time. Typically includes:

    • Lightweight Models: Optimized for speed and efficiency, capable of handling straightforward queries
    • Mid-tier Models: Balanced capabilities for moderately complex questions
    • Heavy-duty Models: Maximum capability models for the most complex or specialized tasks

  • Response Validator: Optional component that evaluates responses from lower-tier models and determines if they need to be escalated to more capable models due to inadequate quality.

  • Performance Monitor: Tracks the effectiveness of routing decisions to enable continuous improvement of the classification and routing rules.

Interactions

How the components work together:

  1. The Complexity Classifier receives an incoming query and analyzes its characteristics to assign a complexity score or category.

  2. The Routing Logic uses this complexity assessment, along with any other relevant factors (system load, user tier, domain type), to select the appropriate model tier.

  3. The selected model processes the query and generates a response.

  4. The Response Validator (if implemented) evaluates the response quality. If it determines the response is inadequate, it can trigger a re-routing to a more capable model.

  5. The Performance Monitor records the routing decision, the models used, and the outcome (whether escalation was needed, user satisfaction metrics, etc.) to inform future routing improvements.

  6. For multi-step interactions, the system may dynamically adjust routing decisions based on the evolving complexity of the conversation.

Consequences

The results and trade-offs of using the pattern:

Benefits:

  • Significant cost reduction by reserving expensive models for complex queries
  • Improved response times for simple queries handled by lightweight models
  • More efficient resource utilization across the entire system
  • Ability to scale to handle larger query volumes cost-effectively
  • Better alignment between query needs and model capabilities

Limitations:

  • Complexity assessment may sometimes misclassify queries, leading to sub-optimal routing
  • Initial implementation requires developing effective complexity classification rules
  • Additional system complexity and potential points of failure
  • Potential for inconsistent user experience if model switching is noticeable
  • May require more extensive monitoring and maintenance than single-model approaches

Performance implications:

  • The complexity classification process adds some overhead to query processing
  • Fallback mechanisms may increase latency for misclassified queries
  • Caching strategies become more complex with multiple model tiers

Implementation

Guidelines for implementing the pattern:

  1. Define Complexity Metrics: Establish clear criteria for what constitutes low, medium, and high complexity queries in your specific domain. Consider factors such as:

    • Query length and linguistic complexity
    • Number of distinct sub-tasks or requirements
    • Domain specificity and technical terminology
    • Need for external knowledge or tool use
    • Presence of ambiguity or contextual understanding requirements

  2. Build a Classifier: Implement a mechanism to classify incoming queries. Options include:

    • Rule-based heuristics for straightforward classification
    • Machine learning classifiers trained on labeled query datasets
    • Using a lightweight LLM specifically for classification purposes
    • Hybrid approaches combining multiple techniques

  3. Establish Model Tiers: Organize available models into clear tiers based on:

    • Capability benchmarks relevant to your domain
    • Cost per token or request
    • Response time distributions
    • Specialized capabilities (code generation, reasoning, creativity)

  4. Implement Routing Logic: Develop the decision-making component that maps complexity classifications to model tiers, incorporating:

    • Confidence thresholds for classification decisions
    • Business rules for specific query types or user segments
    • Load balancing considerations
    • Cost management policies

  5. Create Fallback Mechanisms: Design processes for handling cases where the initially selected model fails to provide an adequate response:

    • Define quality thresholds for responses
    • Implement escalation paths to more capable models
    • Consider user feedback loops for response adequacy

  6. Monitor and Optimize: Establish metrics and monitoring systems to continuously improve routing decisions:

    • Track accuracy of complexity classifications
    • Measure response quality across different routing paths
    • Analyze cost efficiency and identify optimization opportunities
    • Implement feedback loops to refine classification and routing rules

  7. Consider User Experience: Design the system to maintain consistent user experience:

    • Manage response time expectations
    • Consider whether to expose model selection to users
    • Implement seamless transitions between models when escalation occurs

Code Examples

To do...

Variations

Common modifications or adaptations of the basic pattern:

  • Context-Aware Routing: Incorporates conversation history and user context into routing decisions, potentially escalating to more powerful models as conversations become more complex or reference previous exchanges.

  • Domain-Specific Routing: Routes queries not just based on complexity but also based on domain expertise, sending specialized queries to models fine-tuned for specific knowledge domains regardless of apparent complexity.

  • User-Tier Routing: Incorporates user subscription levels or priority tiers into routing decisions, providing premium users with access to more powerful models even for simpler queries.

  • Multi-Model Ensembles: For critical queries, routes the same request to multiple models of different types and combines or selects from their responses based on confidence scores or voting mechanisms.

  • Progressive Processing: Starts with lightweight models and progressively moves to more powerful ones only if necessary, with the lightweight model's output serving as context for the more powerful model.

  • Time-Sensitive Routing: Adjusts routing based on response time requirements, using faster models when immediate responses are needed even if they might provide slightly lower quality.

  • Confidence-Based Routing: Uses model confidence scores or uncertainty metrics to dynamically determine whether to route queries to more capable models.

Real-World Examples

Systems or applications where this pattern has been successfully applied:

  • Enterprise AI Assistants: Large organizations implement tiered model routing to manage costs while maintaining high-quality responses for customer service and employee support applications.

  • Cloud AI Services: Major cloud providers offer API-based services that automatically route queries to different underlying models based on complexity indicators in the request parameters.

  • Search Engine Augmentation: Search engines use complexity routing to determine when to supplement traditional search results with more computationally intensive LLM-generated content.

  • Content Moderation Systems: Platforms route potentially problematic content through increasingly sophisticated analysis models based on initial risk assessment.

  • Healthcare Decision Support: Medical AI systems use lightweight models for standard queries but route complex diagnostic questions to specialized, high-capability models.

Related Patterns

Other patterns that:

  • Router Pattern: A more general pattern for directing requests based on various criteria, of which complexity is just one possible factor.

  • Fallback Chains: Often implemented alongside Complexity-Based Routing to handle cases where the initially selected model fails to provide an adequate response.

  • Semantic Caching: Frequently combined with Complexity-Based Routing to avoid recomputing responses for similar queries.

  • Dynamic Prompt Engineering: Can enhance the effectiveness of different model tiers by optimizing the prompt format for each specific model.

  • Hierarchical Task Decomposition: May use Complexity-Based Routing to assign different sub-tasks to appropriate model tiers based on the complexity of each sub-task.

  • Confidence-Based Human Escalation: Similar conceptual approach but escalates to humans rather than more powerful models when confidence is low.