Semantic Routing Pipelines

Expert

Direct user queries to specialized prompts based on embedding similarity.

01 The Concept

In multi-task systems, sending all queries to a single system prompt leads to rule dilution. Semantic routing embeds the user's query, computes similarity against task clusters, and routes the query to a specialized prompt optimized for that domain.

02 Weak vs. Strong

EX 01Semantic Router Workflow

# 1. Compute embedding user_vector = get_embedding(query) # 2. Check similarity against routes route = get_closest_route(user_vector) # 'billing_route' or 'sales_route' # 3. Call specialized prompt response = call_llm(system_prompt=ROUTE_PROMPTS[route], user_input=query)

→ Why it works

Avoids rule drift. Specialized prompts maintain strict focus, reducing token overhead and preventing rule confusion.

03 Key Points

01Rule dilution: A single massive system prompt failing to maintain constraints.

02Semantic routing: Classification based on vector embeddings rather than hard rules.

03Task specialization: Constructing dedicated small prompts for specific features.

04Latency optimization: Routing simple tasks to smaller models to save costs.

05Gateway pattern: A central dispatcher classifying user inputs at the pipeline boundary.

04 Model-Specific Notes

Claude benefits from specialized contexts: narrow focus prompts prevent instructions from conflicting.

05 For Your Role

It's like a receptionist who directs you to the exact department you need, rather than trying to answer all queries themselves.

Next: Context-Aware Summarization Loops →