Multi-LLM Strategies

Introduction

Multi-LLM uses different models for different steps — cheap classifier first, expensive reasoner only when needed, judge from a third.

Beginner analogy: Like a hospital — receptionist routes, GP diagnoses, specialist treats, auditor reviews.

Understanding the topic

Core concepts to understand:

• Router: cheap small model picks intent.
• Worker: best model for the chosen intent.
• Judge: independent model scores output.
• Fallback: try Claude if GPT fails (or vice versa).
• Frameworks: LangChain, LiteLLM, OpenRouter.

Syntax reference

Visual workflow / architecture:

Query
  ▼ Router (Haiku) → intent
  ▼ Worker (Sonnet | GPT-4o) → answer
  ▼ Judge (Mini) → score
  ▼ Output

Real-world use

Real production systems mix 3–5 models per request to balance cost, quality and resilience.

Best practices

• Start with one model; add routing only after pain.
• Standardise interface via OpenRouter or LiteLLM.
• Test fallback paths regularly.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Why multi-LLM?
• Q2. Router design?
• Q3. How handle fallback?
• Q4. Cost & complexity tradeoff?