The Problem
RAG pipelines that rely solely on vector stores produce answers grounded in stale data. When a user asks about current prices, recent events, or trending topics, the vector store returns documents indexed days or weeks ago. The LLM generates a confident answer based on outdated context, and the user gets wrong information presented as fact.
The Scavio Solution
Add a search grounding step to your RAG pipeline. Before or alongside vector retrieval, classify the query intent. If the query needs fresh data (prices, news, trends, comparisons), query Scavio's search API for current results. Merge the search results with vector store context, giving the LLM both your proprietary knowledge and current web data. The LLM can now cite both sources.
Before
User asks 'What is the current price of the Tesla Model 3?' Vector store returns a document from 3 months ago showing $38,990. Actual current price is $42,490 after a price increase. User makes a purchasing decision based on wrong information.
After
Intent classifier flags 'current price' as freshness-sensitive. Scavio Amazon and Google searches return today's pricing. LLM answers with the current $42,490 price, citing the source. User gets accurate information for $0.01 in API costs (2 searches).
Who It Is For
AI engineers building RAG pipelines who need to handle freshness-sensitive queries without overhauling their existing vector store architecture.
Key Benefits
- Real-time data fills knowledge gaps that vector stores cannot cover
- Intent classification prevents unnecessary API calls on static queries
- Dual-source context improves answer accuracy by 20-40%
- Search results provide citable URLs for answer verification
- Costs $0.005 per search grounding call
Python Example
import requests, os
API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY, "Content-Type": "application/json"}
FRESH_KEYWORDS = ["current", "latest", "today", "price", "cost", "news", "trending"]
def needs_fresh_data(query: str) -> bool:
return any(kw in query.lower() for kw in FRESH_KEYWORDS)
def search_grounding(query: str) -> str:
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers=H,
json={"query": query, "country_code": "us"},
timeout=10,
)
results = resp.json().get("organic_results", [])[:5]
return "\n".join(
f"[{r['title']}]({r['link']}): {r['snippet']}" for r in results
)
def rag_with_grounding(query: str, vector_context: str) -> str:
context_parts = [f"Knowledge base:\n{vector_context}"]
if needs_fresh_data(query):
fresh = search_grounding(query)
context_parts.append(f"Live web results (searched just now):\n{fresh}")
return "\n\n".join(context_parts)
# Example: merge vector store + live search for grounded RAG
vector_ctx = "Tesla Model 3 starts at $38,990 as of Q1 2026..."
grounded = rag_with_grounding("current Tesla Model 3 price", vector_ctx)
print(grounded)JavaScript Example
const API_KEY = process.env.SCAVIO_API_KEY;
const H = {"x-api-key": API_KEY, "Content-Type": "application/json"};
const FRESH_KEYWORDS = ["current", "latest", "today", "price", "cost", "news", "trending"];
function needsFreshData(query) {
return FRESH_KEYWORDS.some(kw => query.toLowerCase().includes(kw));
}
async function searchGrounding(query) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: H,
body: JSON.stringify({ query, country_code: "us" }),
});
const results = (await res.json()).organic_results || [];
return results.slice(0, 5)
.map(r => `[${r.title}](${r.link}): ${r.snippet}`)
.join("\n");
}
async function ragWithGrounding(query, vectorContext) {
const parts = [`Knowledge base:\n${vectorContext}`];
if (needsFreshData(query)) {
const fresh = await searchGrounding(query);
parts.push(`Live web results (searched just now):\n${fresh}`);
}
return parts.join("\n\n");
}
const vectorCtx = "Tesla Model 3 starts at $38,990 as of Q1 2026...";
const grounded = await ragWithGrounding("current Tesla Model 3 price", vectorCtx);
console.log(grounded);Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Amazon
Product search with prices, ratings, and reviews
YouTube
Video search with transcripts and metadata
Walmart
Product search with pricing and fulfillment data
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