Solution

Fix AI Agent Hallucinations with Live Search Grounding

LLM-based agents confidently generate outdated or fabricated facts when answering questions about current events, pricing, product specs, or competitor data. Without live data grou

The Problem

LLM-based agents confidently generate outdated or fabricated facts when answering questions about current events, pricing, product specs, or competitor data. Without live data grounding, every agent response is a liability.

The Scavio Solution

Insert a Scavio search call before the LLM generates its final answer. The agent queries real-time SERP data, injects verified facts into the prompt context, and produces grounded responses with source URLs the user can verify.

Before

AI agent returns plausible but incorrect answers about current pricing, release dates, or product availability. Users lose trust after catching multiple errors.

After

Agent pre-fetches live search results, grounds every factual claim in a real source, and includes citation links so users can verify independently.

Who It Is For

AI agent developers building coding assistants and research tools.

Key Benefits

  • Eliminates hallucinated facts with real-time search grounding
  • Source URLs attached to every factual claim
  • Works with any LLM backend via simple API injection
  • Reduces user trust erosion from incorrect agent outputs

Python Example

Python
import requests

def ground_agent_response(question: str) -> dict:
    search_resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": SCAVIO_API_KEY, "Content-Type": "application/json"},
        json={"query": question, "platform": "google", "limit": 5}
    )
    sources = search_resp.json().get("results", [])
    context = "\n".join(
        f"- {s['title']}: {s['snippet']} ({s['link']})" for s in sources
    )
    return {
        "grounded_context": context,
        "source_urls": [s["link"] for s in sources],
        "prompt_injection": f"Use ONLY these verified sources:\n{context}"
    }

grounding = ground_agent_response("What is the current price of GPT-4o API?")
print(grounding["prompt_injection"])

JavaScript Example

JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
fetch('https://api.scavio.dev/api/v1/search', {method: 'POST', headers: H, body: JSON.stringify({query: 'example', country_code: 'us'})}).then(r => r.json()).then(d => console.log(d.organic_results?.length + ' results'));

Platforms Used

Google

Web search with knowledge graph, PAA, and AI overviews

Reddit

Community, posts & threaded comments from any subreddit

Frequently Asked Questions

LLM-based agents confidently generate outdated or fabricated facts when answering questions about current events, pricing, product specs, or competitor data. Without live data grounding, every agent response is a liability.

Insert a Scavio search call before the LLM generates its final answer. The agent queries real-time SERP data, injects verified facts into the prompt context, and produces grounded responses with source URLs the user can verify.

AI agent developers building coding assistants and research tools.

Yes. Scavio's free tier includes 250 credits per month with no credit card required. That is enough to validate this solution in your workflow.

Fix AI Agent Hallucinations with Live Search Grounding

Insert a Scavio search call before the LLM generates its final answer. The agent queries real-time SERP data, injects verified facts into the prompt context, and produces grounded