16. Multi-Model Setup
Different AI models excel at different tasks. A model optimized for code completion might underperform on natural language explanation. A fast, lightweight model might lack capabilities needed for complex architectural reasoning. Multi-model setups route requests to appropriate models based on task characteristics, cost tolerance, and latency requirements.
The architectural pattern involves a routing layer that classifies incoming requests and dispatches them to suitable models. Classification criteria include task type (code generation versus text explanation), complexity (simple versus multi-step reasoning), language, and required quality level. The router maintains model capabilities inventory and performance metrics to inform routing decisions.
Model selection criteria vary by use case. Code completion for autocomplete needs sub-100ms latency, accepting lower quality for speed. Security review prioritizes detection accuracy over latency, tolerating minutes of processing for thorough analysis. Documentation generation sits in the middleΓÇöusers expect reasonable speed but also good output quality.
Cost management drives many multi-model architectures. Frontier models like GPT-4 and Claude 3 Opus provide best-in-class capabilities at significant per-token cost. Smaller models like CodeLlama or Mistral handle simpler tasks at fraction of the cost. Routing straightforward requests to cheap models while reserving expensive models for complex tasks optimizes the cost-quality tradeoff.
Fallback chains ensure availability. When the preferred model is unavailableΓÇörate limits, maintenance, errorsΓÇöthe router should fall back to alternative models. A well-designed fallback chain provides graceful degradation rather than hard failures. The chain should order models by preference, testing availability and falling through sequentially.
Request transformation bridges model differences. Different models accept different input formats, context lengths, and parameter names. The routing layer should normalize inputs to a standard format, then transform to model-specific formats on dispatch. This abstraction allows adding new models without changing calling code.
Response normalization provides consistent output regardless of source model. Different models produce different response formats, quality levels, and characteristic quirks. Post-processing can standardize outputsΓÇöextracting code blocks, normalizing formatting, filtering low-quality generations. This normalization enables downstream code to treat all model responses uniformly.
Evaluation infrastructure validates routing decisions. Measure task success rate, latency, and cost for each model across task types. Identify models that underperform certain task categories and adjust routing rules accordingly. Continuous evaluation catches model regressions that might invalidate previously sound routing decisions.
Implement a simple router that classifies incoming requests as "simple" or "complex" based on request characteristics, dispatching simple requests to a fast local model and complex requests to a frontier API. Measure latency and quality differences.