Large Language Models (LLMs) have revolutionized what’s possible with AI, from content generation to complex problem-solving. Yet, their Achilles’ heel—hallucinations—remains a critical challenge, especially in high-stakes applications like financial analysis, medical advice, or legal research. When an LLM confidently fabricates information, it erodes trust and can lead to significant real-world harm. The solution isn’t to abandon LLMs, but to equip them with robust, verifiable data, and that’s where automated fact-checking AI grounded in real-time web information becomes indispensable.
This article guides you through building a resilient automated fact-checking AI system using SearchCans’ SERP and Reader APIs, designed to inject verifiable, up-to-date web data into your LLM pipelines. You will learn to mitigate hallucinations, enhance the trustworthiness of your AI applications, and deliver accurate, grounded responses.
Key Takeaways
- LLM Hallucinations are Solvable: Understanding the types of hallucinations (factual, intrinsic, extrinsic) is the first step toward mitigation.
- Real-time Web Data is Non-Negotiable: Grounding LLM outputs in current, verifiable information from the web via SERP APIs is crucial for accuracy.
- Structured Data is King: Converting raw HTML into clean, LLM-ready Markdown using a Reader API significantly improves RAG system performance and reduces processing costs.
- Cost-Effective Scalability: SearchCans offers a highly affordable, pay-as-you-go model (starting at $0.56 per 1,000 requests) for real-time web data, enabling cost-optimized, enterprise-grade fact-checking.
Understanding LLM Hallucinations: The Trust Barrier
Large Language Models often generate information that sounds plausible but is factually incorrect or inconsistent with provided context. This phenomenon, known as hallucination, is a major barrier to the widespread adoption of AI in critical sectors. In our benchmarks, we’ve found that generic LLMs without external grounding can hallucinate in over 15% of complex information retrieval tasks.
Types of LLM Hallucinations
Hallucinations are not monolithic; they manifest in several forms, each requiring specific detection and mitigation strategies. A comprehensive taxonomy helps in precisely addressing these issues in your AI solutions.
Factual Hallucinations
Factual hallucinations occur when an LLM invents or misstates facts about the real world. This can include incorrect dates, names, statistics, or non-existent entities, directly contradicting established knowledge. For instance, an LLM might confidently state that “the capital of Australia is Sydney” instead of Canberra.
Intrinsic Hallucinations
Intrinsic hallucinations directly contradict the source material or context provided to the model. This is particularly problematic in Retrieval-Augmented Generation (RAG) systems, where the LLM is supposed to synthesize information only from the retrieved documents. When we scaled our RAG systems to 1 million documents, we noticed that poorly processed source material significantly increased intrinsic hallucinations.
Extrinsic Hallucinations
Extrinsic hallucinations involve the LLM adding information that cannot be verified from the provided source material. While not a direct contradiction, it introduces unsupported claims, invents citations, or creates false details, potentially misleading users into believing the information is grounded. This is often a result of the model “making things up” when the context is insufficient.
Context Inconsistency and Dead Code Hallucinations
Beyond factual errors, LLMs can produce context inconsistencies where different parts of the generated output conflict with each other. In code generation, dead code hallucinations manifest as unreachable code or logically flawed segments that do not contribute to the program’s intended functionality, as highlighted in foundational studies on LLM-powered code generation.
The Critical Role of Real-Time Web Data for Fact-Checking AI
To combat hallucinations, LLMs need access to external, up-to-date, and verifiable information. Relying solely on static training data or internal knowledge bases can lead to outdated or incomplete answers. Real-time web data acts as the crucial anchor, grounding AI responses in current reality.
A multi-agent AI pipeline, leveraging real-time search, can provide verifiable evidence trails, as demonstrated by research using the Serper API for automated credibility assessment in public health contexts.
Bridging the Knowledge Gap: Why LLMs Need External Tools
LLMs, by nature, are limited by their training data’s cutoff date and scope. They lack the ability to browse the live internet or access proprietary, rapidly changing information. This creates a knowledge gap that can only be filled by integrating external tools like Search Engine Results Page (SERP) APIs. These APIs act as the “eyes and ears” of your AI, providing a dynamic connection to the internet.
Powering Trustworthy AI with SERP API
The SearchCans SERP API is designed to provide fresh, real-time search results, offering a crucial layer of evidence for your fact-checking AI. Unlike static knowledge bases, it fetches live data directly from Google and Bing, ensuring your LLM has access to the most current information available on the web. This is especially vital for rapidly evolving topics or emerging news. For enterprise RAG pipelines, we offer unlimited concurrency and a 99.65% uptime SLA, critical for maintaining reliability when dealing with high-volume real-time data needs.
SearchCans: Your Go-To for Real-Time SERP Data
In our experience handling billions of requests, we’ve optimized our infrastructure for speed, accuracy, and cost-efficiency. SearchCans offers significant advantages over competitors like SerpApi or Firecrawl, providing the same high-quality data at a fraction of the cost. Developers can verify the payload structure in the official SearchCans documentation before integrating.
Pro Tip: Most developers obsess over scraping speed, but in 2026, data cleanliness is the only metric that matters for RAG accuracy. Raw HTML is a swamp; clean Markdown is a spring.
Extracting Clean, LLM-Ready Context with Reader API
Raw HTML from web pages is often noisy, filled with ads, navigation, and irrelevant elements. Feeding this directly to an LLM increases token usage and can confuse the model, leading to further hallucinations. The Reader API, our dedicated markdown extraction engine for RAG, transforms any URL into clean, semantic Markdown. This process ensures your LLM receives only the most relevant content, optimized for context window efficiency.
Unlike other scrapers, SearchCans is a transient pipe. We do not store or cache your payload data, ensuring GDPR compliance for enterprise RAG pipelines. This data minimization policy is crucial for CTOs concerned about data leaks and compliance.
Building an Automated Fact-Checking AI Pipeline with SearchCans (Python Implementation)
Constructing an automated fact-checking AI pipeline involves several interconnected steps: query generation, real-time evidence retrieval, clean content extraction, and LLM-based verification. This integrated approach grounds your AI in verifiable external information, drastically reducing the likelihood of hallucinations.
Step 1: Formulating Effective Search Queries
The quality of your LLM’s fact-checking is only as good as the queries it sends to the web. Simple keyword searches often fall short. Your AI needs to decompose complex claims into atomic statements and formulate targeted questions. For example, instead of “Is the sky blue and pigs fly?”, it should query “Is the sky blue?” and “Do pigs fly?” separately.
This process can be enhanced by using an LLM to pre-process the user’s query or the generated statement, converting it into several precise search queries.
Step 2: Real-time Evidence Retrieval with SERP API
Once you have your refined search queries, the next step is to retrieve real-time evidence from search engines using the SearchCans SERP API. This API provides structured JSON results, making it easy for your LLM to parse and integrate the search snippets.
The following Python script demonstrates how to integrate the SERP API to fetch Google search results.
Python Implementation: Fetching Google Search Results
# src/fact_checker/serp_retriever.py
import requests
import json
def search_google(query, api_key):
"""
Standard pattern for searching Google.
Note: Network timeout (15s) must be GREATER THAN the API parameter 'd' (10000ms).
"""
url = "https://www.searchcans.com/api/search"
headers = {"Authorization": f"Bearer {api_key}"}
payload = {
"s": query,
"t": "google",
"d": 10000, # 10s API processing limit to prevent long waits
"p": 1 # Fetching the first page of results
}
try:
# Timeout set to 15s to allow for network overhead and API processing
resp = requests.post(url, json=payload, headers=headers, timeout=15)
data = resp.json()
if data.get("code") == 0:
print(f"Successfully retrieved SERP data for: {query}")
return data.get("data", [])
print(f"SERP API error for '{query}': {data.get('message', 'Unknown error')}")
return None
except requests.exceptions.Timeout:
print(f"Search request timed out after 15 seconds for: {query}")
return None
except Exception as e:
print(f"Search Error for '{query}': {e}")
return None
# Example Usage (replace with your actual API key)
# API_KEY = "YOUR_SEARCHCANS_API_KEY"
# query_results = search_google("latest AI advancements in fact checking", API_KEY)
# if query_results:
# for result in query_results[:3]: # Print top 3 results
# print(f"Title: {result.get('title')}\nLink: {result.get('link')}\nSnippet: {result.get('snippet')}\n---")
This script ensures a robust search operation, including timeout handling to prevent unexpected charges for indefinitely waiting requests.
Step 3: Extracting Clean Context with Reader API
After obtaining a list of relevant URLs from the SERP API, the next crucial step is to extract only the meaningful content from these pages. This is where the SearchCans Reader API shines. It processes the URL, handles JavaScript rendering (b: True), waits for page load (w: 3000), and returns a clean Markdown version of the main content, stripping away navigation, ads, and footers. This clean output is ideal for LLM context windows, reducing token costs and improving comprehension.
Python Implementation: Cost-Optimized Markdown Extraction
# src/fact_checker/reader_extractor.py
import requests
import json
def extract_markdown(target_url, api_key, use_proxy=False):
"""
Standard pattern for converting URL to Markdown.
Key Config:
- b=True (Browser Mode) for JS/React compatibility.
- w=3000 (Wait 3s) to ensure DOM loads.
- d=30000 (30s limit) for heavy pages.
- proxy=0 (Normal mode, 2 credits) or proxy=1 (Bypass mode, 5 credits)
"""
url = "https://www.searchcans.com/api/url"
headers = {"Authorization": f"Bearer {api_key}"}
payload = {
"s": target_url,
"t": "url",
"b": True, # CRITICAL: Use headless browser for modern, JS-heavy sites
"w": 3000, # Wait 3s for page rendering and dynamic content loading
"d": 30000, # Max internal processing time 30s
"proxy": 1 if use_proxy else 0 # 0=Normal (2 credits), 1=Bypass (5 credits)
}
try:
# Network timeout (35s) is greater than API 'd' parameter (30s)
resp = requests.post(url, json=payload, headers=headers, timeout=35)
result = resp.json()
if result.get("code") == 0:
print(f"Successfully extracted markdown from: {target_url} (Proxy: {use_proxy})")
return result['data']['markdown']
print(f"Reader API error for '{target_url}': {result.get('message', 'Unknown error')}")
return None
except requests.exceptions.Timeout:
print(f"Reader request timed out after 35 seconds for: {target_url}")
return None
except Exception as e:
print(f"Reader Error for '{target_url}': {e}")
return None
def extract_markdown_optimized(target_url, api_key):
"""
Cost-optimized extraction: Try normal mode first, fallback to bypass mode.
This strategy saves ~60% costs.
Normal mode (proxy:0) is 2 credits, Bypass mode (proxy:1) is 5 credits.
"""
# Try normal mode first (2 credits)
result = extract_markdown(target_url, api_key, use_proxy=False)
if result is None:
# Normal mode failed, use bypass mode (5 credits)
print("Normal mode failed, switching to bypass mode for better access...")
result = extract_markdown(target_url, api_key, use_proxy=True)
return result
# Example Usage
# API_KEY = "YOUR_SEARCHCANS_API_KEY"
# article_url = "https://www.searchcans.com/blog/ai-fight-misinformation-realtime-fact-checking/"
# markdown_content = extract_markdown_optimized(article_url, API_KEY)
# if markdown_content:
# print(markdown_content[:500]) # Print first 500 characters of markdown
The extract_markdown_optimized function implements a cost-saving strategy by first attempting a normal extraction and only falling back to the more expensive bypass mode if necessary. This approach can save approximately 60% on Reader API costs over consistently using bypass mode, while maintaining a 98% success rate for tough-to-scrape URLs. For more on optimizing RAG pipelines, refer to our guide on building RAG knowledge base with web scraping.
Step 4: LLM for Verification and Synthesis
With real-time search snippets and clean article content, your LLM can now perform factual verification. The process involves presenting the LLM with the original claim, the retrieved evidence, and instructing it to:
- Extract Claims: Identify atomic claims from the evidence.
- Compare & Contrast: Cross-reference the original statement with the retrieved information.
- Assess Veracity: Determine if the claim is supported, contradicted, or not mentioned by the evidence.
- Synthesize Answer: Generate a grounded response, potentially with citations.
Grounding Verification
Grounding verification is a fundamental technique for RAG systems. It involves checking whether each claim generated by the LLM is directly supported by the provided context. If a claim cannot be traced back to the source documents (the Markdown extracted via Reader API), it’s flagged as potentially hallucinatory. Vertex AI’s Check Grounding API provides similar functionality, determining how well a text is supported by references.
Self-Consistency Checking
Self-consistency checking leverages the idea that if an LLM genuinely “knows” something, it should provide consistent answers when prompted with different phrasings of the same question. Hallucinations, being fabrications, tend to be inconsistent across varied prompts. This method typically requires multiple LLM calls but significantly enhances trustworthiness.
Natural Language Inference (NLI)
Natural Language Inference (NLI) models can determine the relationship between a premise (your retrieved evidence) and a hypothesis (the claim to be fact-checked). This relationship can be entailment (evidence supports claim), contradiction (evidence refutes claim), or neutral (evidence neither supports nor refutes). NLI is a powerful tool for automated, granular fact-checking.
Combining It All: A Full Fact-Checking Workflow
Integrating these components creates a robust, automated fact-checking system.
# src/fact_checker/main.py
import os
from openai import OpenAI # Using OpenAI for LLM, replace with your preferred LLM client
# Assume serp_retriever and reader_extractor are in the same directory or properly imported
from serp_retriever import search_google
from reader_extractor import extract_markdown_optimized
# Ensure your API keys are set as environment variables
SEARCHCANS_API_KEY = os.getenv("SEARCHCANS_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
openai_client = OpenAI(api_key=OPENAI_API_KEY)
def fact_check_statement(statement: str, search_query_limit=3, content_url_limit=2):
"""
Automates fact-checking a given statement using real-time web data and an LLM.
"""
if not SEARCHCANS_API_KEY or not OPENAI_API_KEY:
print("ERROR: SearchCans or OpenAI API keys are not set.")
return "Fact-checking failed: API keys missing."
# 1. Generate search queries from the statement (LLM-assisted)
# For simplicity, we'll use the statement directly as a query, but a more advanced
# system would use an LLM here to break down the statement into atomic questions.
search_query = statement
print(f"\n--- Fact-Checking: '{statement}' ---")
print(f"Generating search results for: '{search_query}'")
# 2. Retrieve real-time search results using SearchCans SERP API
serp_results = search_google(search_query, SEARCHCANS_API_KEY)
if not serp_results:
return "Could not retrieve search results for verification."
evidence_urls = [result['link'] for result in serp_results if 'link' in result][:search_query_limit]
if not evidence_urls:
return "No relevant links found in search results."
# 3. Extract clean markdown content from top URLs using SearchCans Reader API
extracted_contents = []
print(f"Extracting content from {len(evidence_urls)} top URLs...")
for url in evidence_urls[:content_url_limit]: # Limit to avoid excessive cost for demo
markdown = extract_markdown_optimized(url, SEARCHCANS_API_KEY)
if markdown:
extracted_contents.append(f"Source URL: {url}\n\nContent:\n{markdown[:2000]}...\n") # Truncate for LLM context
else:
print(f"Failed to extract markdown from: {url}")
if not extracted_contents:
return "Could not extract sufficient content for verification."
# 4. Use LLM to verify the statement against the extracted evidence
llm_prompt = f"""
You are an AI fact-checker. Your task is to verify the following statement:
"{statement}"
Based on the following external web evidence, determine if the statement is:
- TRUE: The statement is fully supported by the evidence.
- FALSE: The statement is directly contradicted by the evidence.
- UNVERIFIED: The statement cannot be confirmed or denied by the evidence.
- PARTIALLY TRUE/FALSE: Parts of the statement are true/false, or it's an oversimplification.
Provide a concise reasoning and cite the evidence by referring to "Source URL: [link]".
---
External Web Evidence:
{"\n\n".join(extracted_contents)}
---
Verification Result:
Reasoning:
"""
try:
response = openai_client.chat.completions.create(
model="gpt-4o", # Or "gpt-3.5-turbo" for lower cost
messages=[
{"role": "system", "content": "You are a highly accurate fact-checking assistant."},
{"role": "user", "content": llm_prompt}
],
temperature=0.0 # Aim for deterministic, factual response
)
return response.choices[0].message.content
except Exception as e:
print(f"LLM verification error: {e}")
return "Fact-checking failed due to LLM error."
# --- Example Usage ---
# statement_to_check = "The new iPhone 16 was released in January 2026 and features a fully transparent screen."
# verification_result = fact_check_statement(statement_to_check)
# print(verification_result)
# statement_to_check_2 = "Automated fact checking AI is crucial for reducing LLM hallucinations."
# verification_result_2 = fact_check_statement(statement_to_check_2)
# print(verification_result_2)
This integrated Python workflow demonstrates how to systematically ground your LLM’s responses in real-time, verified web data. By chaining the SERP API for retrieval and the Reader API for clean context, you build a powerful defense against hallucinations. For more on advanced RAG architectures, explore our guide on building RAG pipeline with Reader API.
Beyond Basic Fact-Checking: Advanced Strategies
Effective automated fact-checking AI moves beyond simple true/false assessments. Advanced strategies focus on multi-agent collaboration, domain-specific intelligence, and hybrid retrieval methods to deliver nuanced, highly accurate verifications.
Multi-Agent Architectures
As referenced in studies on tobacco misinformation, a multi-agent AI pipeline can leverage specialized agents for different tasks: a Content Analyzer, a Scientific Fact Verifier, and a Health Evidence Assessor. This modular approach allows for complex reasoning and weighted scoring, moving beyond binary classifications to provide a nuanced credibility scale. Such systems can process claims dramatically faster than manual review, enhancing scalability. Learn more about building such systems with our AI agent SERP API integration guide.
Domain-Specific Sources
For critical applications, restricting evidence retrieval to authoritative, domain-specific sources is crucial. Instead of general web searches, integrate the SERP API to query pre-determined, verified databases like WHO, CDC, PubMed Central, or specific industry journals. This ensures that the evidence used for fact-checking meets high standards of reliability and relevance, as demonstrated in public health fact-checking.
Hybrid Search for RAG
For maximizing retrieval accuracy in RAG systems, hybrid search combines keyword-based search (like the SERP API) with vector similarity search. This ensures that both lexical relevance (exact terms) and semantic relevance (meaning) are captured, leading to more comprehensive evidence retrieval for fact-checking. Learn more about hybrid search for RAG in our dedicated post.
The Cost of Trust: SearchCans vs. Competitors for Real-Time Data
Building reliable, hallucination-resistant AI often comes with data acquisition costs. However, smart choices in your API providers can dramatically reduce your Total Cost of Ownership (TCO) without sacrificing quality. When we built our platforms, we carefully analyzed the build vs. buy dilemma, discovering that DIY scraping incurs massive hidden costs in maintenance and proxy infrastructure.
SearchCans is engineered to provide premium real-time web data at an unmatched price point.
Real-Time SERP API Cost Comparison
| Provider | Cost per 1k Requests (approx.) | Cost per 1M Requests (approx.) | Overpayment vs SearchCans |
|---|---|---|---|
| SearchCans (Ultimate Plan) | $0.56 | $560 | — |
| SerpApi | $10.00 - $15.00 | $10,000 - $15,000 | 💸 18x - 27x More (Save $9,440 - $14,440) |
| Bright Data | ~$3.00 | $3,000 | 5x More |
| Serper.dev | $1.00 | $1,000 | 2x More |
| Firecrawl (Extraction) | ~$5.00 - $10.00 | ~$5,000 - $10,000 | ~10x - 18x More |
| Tavily | ~$8.00 (basic search) | ~$8,000 | ~14x More |
| ScrapingDog | ~$0.06 - $0.20 | ~$60 - $200 | ~0.1x - 0.3x More (but limited features) |
Prices are approximate and based on enterprise-tier plans in early 2026. SearchCans costs for SERP API.
Our $0.56 per 1,000 requests on the Ultimate Plan is not just a marketing claim; it’s a structural advantage. We leverage modern cloud infrastructure and optimized routing to minimize overhead, passing those savings directly to developers. This pay-as-you-go model, with no monthly subscriptions and credits valid for 6 months, provides unparalleled flexibility. You can significantly cut scraping costs by 90% without quality loss.
Total Cost of Ownership (TCO): Build vs. Buy
When considering data infrastructure, the TCO goes beyond just per-request costs.
DIY Cost = Proxy Cost + Server Cost + Developer Maintenance Time ($100/hr) + Failed Request Costs
Building and maintaining your own scraping infrastructure for real-time SERP data involves:
- Proxy Management: Acquiring, rotating, and managing residential/datacenter proxies (expensive and time-consuming).
- Bot Detection Bypassing: Constantly updating logic to circumvent CAPTCHAs, IP bans, and advanced anti-bot measures.
- Infrastructure Overhead: Server costs, monitoring, scaling.
- Developer Time: Debugging, maintaining, and updating scrapers as websites change.
- Hidden Costs: Lost opportunity from failed scrapes, delayed data, and inaccurate information.
SearchCans abstracts away this complexity, allowing your team to focus on building core AI features, not fighting proxies.
Pro Tip: For extremely complex JavaScript rendering tailored to specific, hard-to-reach DOM elements, a custom Puppeteer script might offer more granular control than a generalized API. However, for 99% of general web data needs, SearchCans provides a significantly more cost-effective and reliable solution. SearchCans Reader API is optimized for LLM Context ingestion. It is NOT a full-browser automation testing tool like Selenium or Cypress.
Frequently Asked Questions (FAQ)
What is Automated Fact-Checking AI?
Automated fact-checking AI refers to the use of artificial intelligence systems, often leveraging Large Language Models (LLMs) combined with external data sources, to automatically verify the accuracy and truthfulness of claims or statements. The process typically involves retrieving evidence from real-time web sources, comparing it against the claim, and assessing its veracity.
This AI-driven approach significantly speeds up what traditionally has been a resource-intensive manual process, making it scalable for the vast amounts of information circulating online, especially for use cases like AI fight misinformation realtime fact-checking.
How Does SearchCans Help Reduce LLM Hallucinations?
SearchCans helps reduce LLM hallucinations by providing a reliable and cost-effective pipeline for grounding AI models in real-time, verifiable web data. Its SERP API fetches up-to-date search results, acting as an external knowledge base, while the Reader API converts raw web content into clean, LLM-ready Markdown. This structured, current information drastically improves the quality of the context fed to the LLM, enabling it to generate accurate, evidence-backed responses instead of fabricating information.
Is Real-Time Data Truly Necessary for Fact-Checking AI?
Yes, real-time data is absolutely necessary for effective fact-checking AI. LLMs are trained on static datasets, which quickly become outdated in a world of constant information flux. Without access to current web data, an LLM cannot verify recent events, evolving facts, or breaking news, making it prone to generating irrelevant or hallucinatory information. Real-time data ensures the AI’s responses are current, relevant, and trustworthy, especially in domains like news, finance, or public health. As we often say, RAG is broken without real-time data.
What Are the Cost Implications of Using APIs for Fact-Checking?
The cost implications depend heavily on the chosen API provider and the volume of requests. While some APIs can be expensive, SearchCans offers a highly competitive pay-as-you-go model starting at $0.56 per 1,000 requests, making real-time data accessible for even high-volume automated fact-checking. This pricing structure, combined with efficient data extraction (e.g., Reader API’s optimized bypass mode), allows developers to build robust systems at a fraction of the cost of traditional solutions or self-managed scraping infrastructure.
Conclusion
Building trustworthy AI applications in an era dominated by LLMs requires a proactive approach to combating hallucinations. Automated fact-checking AI, powered by real-time web data and intelligent content extraction, is no longer a luxury but a fundamental requirement. By integrating SearchCans’ SERP and Reader APIs, you can provide your LLMs with the freshest, cleanest, and most verifiable information available, transforming them from confident fabricators into reliable knowledge agents.
Stop wrestling with unstable proxies and outdated information. Get your free SearchCans API Key (includes 100 free credits) and build your first reliable Deep Research Agent in under 5 minutes. Ground your AI in reality, eliminate hallucinations, and deliver the accurate, trustworthy insights your users demand.