An r/ClaudeCode user ran $42K of Claude API through a $500 plan — 84x leverage. One overlooked cost reducer: search grounding prevents hallucination retries. One $0.005 search call can save a $0.10+ LLM retry cycle.
Prerequisites
- Scavio API key
- LLM API access
- Python 3.8+
Walkthrough
Step 1: Identify retry-prone queries
Factual questions cause the most retries due to hallucination.
Python
# High-retry categories:
# - Current pricing/versions (changes frequently)
# - Company/product facts (LLM training data is stale)
# - Recent events (not in training data)
# These benefit most from search groundingStep 2: Add search grounding before LLM call
Fetch current facts, inject into prompt.
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def grounded_query(question):
context = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json={'platform': 'google', 'query': question}).json()
# Inject search results into LLM prompt
prompt = f'Answer based on these current search results:\n{context}\n\nQuestion: {question}'
return promptStep 3: Measure the savings
Compare token usage with and without grounding.
Text
# Without grounding:
# Query → LLM hallucinates → user catches → retry → correct answer
# Cost: 2-3x the tokens (original + retry + correction)
#
# With grounding:
# Query → search ($0.005) → LLM answers correctly first time
# Cost: 1x tokens + $0.005 search
# Net savings: 50-66% on factual queriesStep 4: Route selectively
Only ground factual queries, not reasoning tasks.
Python
def should_ground(question):
factual_signals = ['current', 'price', 'latest', 'how much', 'when did', 'who is']
return any(s in question.lower() for s in factual_signals)
def smart_query(question):
if should_ground(question):
return grounded_query(question)
return direct_llm_query(question)Python Example
Python
# ROI math: 100 factual queries/day
# Without grounding: 100 × 2.5 retries × $0.03/call = $7.50/day
# With grounding: 100 × $0.005 search + 100 × $0.03 = $3.50/day
# Savings: $4/day = $120/moJavaScript Example
JavaScript
// Same routing pattern in JS/TS.Expected Output
JSON
Selective search grounding that reduces LLM hallucination retries. 50-66% token savings on factual queries.