𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻: 𝗪𝗵𝗲𝗻 𝗢𝗻𝗲 𝗠𝗼𝗱𝗲𝗹 𝗜𝘀𝗻'𝘁 𝗘𝗻𝗼𝘂𝗴𝗵
Single model systems are easy. Multi-model systems are strong. The goal is not to pick many models. The goal is to build the right architecture to manage them.
You need the right model for the right task at the right time.
Use these five patterns to build your system:
Sequential Patterns • Pipeline: One model's output feeds the next model. Use this for multi-step workflows. • Router: A small model classifies the task and sends it to a specialist. Use this to save costs.
Parallel Patterns • Fan-out: Run the same prompt through multiple models at once. Use this for A/B testing or comparing results. • Voting: Get answers from several models and pick the most common one. Use this for classification tasks.
Hierarchical Patterns • Planner-executor: A large model creates a plan. Small models carry out the steps. Use this to manage complex tasks cheaply. • Supervisor-worker: A supervisor model assigns tasks and reviews the work. Use this for high-quality control.
Ensemble Patterns • Weighted ensemble: Give different models different scores based on how much you trust them. Pick the highest score. • Consensus ensemble: Require models to agree before making a decision. Use this for critical tasks.
Trade-offs to remember:
- Single Model: Lowest cost and lowest complexity.
- Sequential: Higher latency because models wait for each other.
- Parallel: Higher cost because you run many models at once.
- Hierarchical: Highest quality but very complex to build.
- Ensemble: Highest cost and highest complexity.
Follow this rule: Start with one model. Add more models only when you face a problem with cost, speed, or quality. Do not build complexity before you need it.
Source: https://dev.to/rosgluk/multi-model-system-design-when-one-model-isnt-enough-311c
Optional learning community: https://t.me/GyaanSetuAi