Ground LLM Responses in Real Data

The Problem

Users no longer tolerate confident-sounding hallucinations. When an assistant cites a statistic that does not exist, attributes a quote to the wrong person, or invents a product that was never released, trust in the entire product collapses. Grounding is the accepted fix, but most grounding sources are either too narrow (one domain, one document set) or too stale (last month's crawl). The LLM ends up either refusing interesting questions or confidently making things up. Neither is acceptable once the assistant is in front of real customers.

The Scavio Solution

Use Scavio as the grounding layer for any question that benefits from live external data. The pipeline is simple: classify whether the question needs grounding, call Scavio, insert the cited snippets into the prompt with source URLs, and instruct the model to only answer using those snippets. The model stops hallucinating because it has been explicitly bound to verifiable content, and users see citations they can click on. Because Scavio covers Google, YouTube, Amazon, and Walmart, the grounding source works for factual queries, tutorials, product questions, and more.

Who It Is For

Product teams shipping user-facing LLM features. If your support, sales, or research assistant cannot afford to hallucinate and your users expect clickable citations, grounding with Scavio is the minimum bar.

Key Benefits

• Live sources with URLs the user can verify
• Drops directly into any grounding prompt template
• Covers factual web, product, and video queries from one API
• Sub-two-second latency keeps grounded answers feeling fast
• Works with OpenAI, Anthropic, Gemini, and open models

Python Example

import requests

API_KEY = "your_scavio_api_key"

SYSTEM = (
    "Answer the question using only the provided sources. "
    "Cite each claim with the source number in brackets."
)

def ground(query: str):
    r = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": "google", "query": query},
        timeout=10,
    )
    sources = r.json().get("organic", [])[:5]
    context = "\n".join(
        f"[{i+1}] {s['title']}: {s['snippet']} ({s['link']})"
        for i, s in enumerate(sources)
    )
    return SYSTEM, context

sys, ctx = ground("latest iPhone battery life tests")
print(sys, ctx, sep="\n\n")

JavaScript Example

const API_KEY = "your_scavio_api_key";

const SYSTEM =
  "Answer the question using only the provided sources. Cite each claim with the source number in brackets.";

async function ground(query) {
  const r = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: {
      "x-api-key": API_KEY,
      "content-type": "application/json",
    },
    body: JSON.stringify({ platform: "google", query }),
  });
  const data = await r.json();
  const sources = (data.organic ?? []).slice(0, 5);
  const context = sources
    .map((s, i) => `[${i + 1}] ${s.title}: ${s.snippet} (${s.link})`)
    .join("\n");
  return { system: SYSTEM, context };
}

console.log(await ground("latest iPhone battery life tests"));

Platforms Used