Prompt Ensembling via MoE

Expert

Route prompt segments to specialized models and merge outputs.

01 The Concept

Prompt Ensembling via Mixture of Experts (MoE) coordinates multiple specialized models to solve complex tasks.

A routing prompt classifies the input domain, sends tasks to domain-specialized LLMs (e.g. coding to Claude, JSON to GPT, search to Gemini), and a final synthesis prompt compiles their outputs into a single consolidated response, maximizing performance.

02 Weak vs. Strong

EX 01MoE Routing Pipeline (Python)

# 1. Route task category = call_llm(model="gpt-3.5-turbo", prompt="Classify task (code/search): " + user_query) # 2. Call specialist if category == "code": expert_output = call_llm(model="claude-3-5-sonnet", prompt=user_query) else: expert_output = call_llm(model="perplexity-sonar", prompt=user_query) # 3. Format response final_out = call_llm(model="gpt-4o-mini", prompt=f"Format: {expert_output}")

→ Why it works

Routes task segments dynamically to specialists. By using Sonnet for coding and Perplexity for live search, you get the best outcome for each domain.

03 Key Points

01Expert Routing: Classifying the input query to select the best model for the job.

02Multi-Model calls: Dispatching sub-tasks to Claude, GPT, or Gemini in parallel.

03Handoff contracts: Defining strict output schemas for each sub-model to pass data.

04Synthesis Compilation: Fusing expert outputs into a single cohesive response.

05Efficiency optimization: Using cheap models for routing and expensive models only for hard tasks.

04 Model-Specific Notes

Claude functions well as the 'Synthesis Coder' or 'Analytical Expert' in MoE routing pipelines.

05 For Your Role

It's like having a manager who receives customer mail, forwards technical questions to engineers, pricing questions to sales, and drafts the reply.

Next: Self-Refinement & Critics →