Pattern: Router + Fallback

Route each query to the cheapest retriever that can handle it; fall back to progressively more powerful (and expensive) retrievers only when cheaper ones fail.

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Problem

A single retrieval strategy cannot be optimal for all query types. Dense semantic search handles paraphrase and concept queries well but fails on exact-match lookups (product codes, names, citations). BM25 handles exact match but misses semantic intent. Agentic loops handle complex multi-hop queries but add 5–10× latency and cost for simple queries that a single retrieval call could answer.

Without routing, every query pays the cost of the most expensive path even when a cheaper one would suffice.

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Solution: Router + Fallback Chain

Query
  │
  ▼
┌─────────────────┐
│  Query Router   │  ← classifies query type (fast, cheap)
└────────┬────────┘
         │
   ┌─────┴──────────────────────────────────────┐
   │                                            │
   ▼                                            ▼
TIER 1: Semantic Cache            TIER 2: Dense Retrieval
(hit → return immediately)        (most queries land here)
   │                                            │
   │ cache miss                                 │ low confidence
   ▼                                            ▼
TIER 3: Hybrid (dense + BM25)       TIER 4: Agentic Loop
(exact match + semantic)            (complex multi-hop only)

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Router Implementation

import anthropic
import json

client = anthropic.Anthropic()

ROUTER_PROMPT = """Classify the user's query into exactly one category:
- "cache_lookup": greeting, trivial, or recently asked
- "exact_match": looking for a specific identifier, name, code, or citation
- "semantic": conceptual question answerable from a single passage
- "hybrid": requires both keyword precision and semantic understanding
- "agentic": requires multi-step reasoning, comparison across many documents, or synthesis

Output JSON: {"type": "...", "confidence": 0.0-1.0}"""

def route_query(query: str) -> dict:
    resp = client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=64,
        system=ROUTER_PROMPT,
        messages=[{"role": "user", "content": query}],
    )
    return json.loads(resp.content[0].text)


def retrieve_with_fallback(query: str, semantic_cache, vector_db, bm25_index, agent_fn) -> list[dict]:
    route = route_query(query)
    query_type = route["type"]
    
    # Tier 1: semantic cache
    cached = semantic_cache.get(query)
    if cached:
        return cached
    
    # Tier 2: pure dense (most queries)
    if query_type in ("semantic",):
        results = vector_db.search(query, k=5)
        if results and results[0]["score"] > 0.75:
            return results
        # Score too low → fall through to hybrid
        query_type = "hybrid"
    
    # Tier 3: hybrid dense + BM25
    if query_type in ("exact_match", "hybrid"):
        dense_results = vector_db.search(query, k=10)
        bm25_results  = bm25_index.search(query, k=10)
        results = rrf_merge(dense_results, bm25_results, k=5)
        if results and results[0]["score"] > 0.5:
            return results
        query_type = "agentic"
    
    # Tier 4: agentic loop (expensive — last resort)
    if query_type == "agentic":
        return agent_fn(query)
    
    return []

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Fallback Trigger Conditions

Trigger	Condition	Action
Cache miss	Cosine similarity < threshold	Drop to dense retrieval
Low retrieval confidence	Top-1 score < 0.75	Drop to hybrid
Empty results	len(results) == 0	Drop to next tier
Query complexity signal	Router returns "agentic"	Skip to agentic tier directly
Retrieval timeout	Response > 500ms	Return cached degraded result

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Cost and Latency by Tier

Tier	Latency	Cost per query	When Used
Semantic cache	<10ms	~$0.000001	~20% of queries
Dense only	30–100ms	~$0.001	~60% of queries
Hybrid	100–300ms	~$0.003	~15% of queries
Agentic loop	2–10s	~$0.05–$0.20	~5% of queries

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Anti-patterns

• Routing to agentic by default: even a 10% agentic rate with $0.10/query = $10K/month at 1M queries/month.
• No fallback from cache: a stale cache answer for a fresh query erodes trust fast.
• Over-routing to hybrid: hybrid is only better than dense for keyword-sensitive queries (~15–20% of typical traffic).
• Routing on query length alone: long queries are not necessarily complex; short queries are not necessarily simple.

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Interview Q&A

Q: How would you design a query router for a production RAG system?

A practical router has three components: (1) a rule-based pre-filter for deterministic cases (explicit entity codes → exact match tier; greetings → cache); (2) a lightweight classifier (Haiku or a fine-tuned small model) for semantic classification of query intent; (3) confidence-based promotion — even if the router says "semantic," if the retrieved results score poorly, fall through to the next tier automatically. The router itself should add <50ms latency and cost <$0.0001 per call to remain net-positive. Track "tier distribution" as a KPI — if the agentic tier grows above 10% of traffic, investigate whether the router is miscategorizing or the query mix has genuinely shifted.