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LLM Grounding & Evaluation

Build AI systems that fact-check model outputs, validate training data, and ground language models in real-world information. Create evaluation workflows that test model accuracy against live web data, verify claims through multi-source validation, and maintain model reliability through continuous real-world grounding.

Learn Fact-Checking

Understand fact-checking workflows

Get Started

Start validating model outputs

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Challenges Addressed

Handle the unique challenges of AI evaluation at scale:
  • Real-time fact verification - Requiring fast web access for immediate validation
  • Comprehensive testing - Demanding broad source coverage for thorough evaluation
  • Historical validation - Needing archive access for fact-checking historical claims
  • Continuous evaluation - Requiring reliable infrastructure that never goes down
From simple fact-checking to comprehensive model evaluation frameworks, grounding systems need infrastructure that provides both speed and reliability.

Fast Web Access

Real-time fact verification with sub-second response times

Broad Source Coverage

Comprehensive testing across multiple sources

Historical Validation

Access historical data for fact-checking past claims

Reliable Infrastructure

99.99% uptime ensures continuous evaluation never stops

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Goal

Built for evaluation patterns that maintain model accuracy and user trust through rigorous real-world validation.

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Fact-Checking Workflows

Verify claims against real-world data:
1

Extract Claims from Model Output

Extract factual claims from model outputs that need verification.
{
  "claims": [
    {
      "text": "The company was founded in 2020",
      "entity": "company_name",
      "type": "factual"
    }
  ]
}
2

Search for Verification

Search for verification across multiple sources:
  • Real-time search results (SERP API)
  • Historical data (Web Archive)
  • Structured data (Deep Lookup)
async function verifyClaim(claim) {
  const searches = await Promise.all([
    searchSERP(claim.text),
    searchArchive(claim.entity, '2020-01-01'),
    searchDeepLookup(claim.entity)
  ]);
  
  return searches;
}
3

Validate Against Sources

Validate claims against multiple sources and determine confidence.
Use cross-source validation to increase confidence in fact-checking results.
4

Report Validation Results

Report validation results with source attribution and confidence scores.
Validated claims are marked with confidence scores and source references.

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Model Output Validation

Validate model outputs in real-time: 
async function validateModelOutput(output, claims) {
  const validationPromises = claims.map(claim => 
    verifyClaim(claim)
  );
  
  const validationResults = await Promise.all(validationPromises);
  
  const validatedOutput = {
    original: output,
    claims: validationResults.map((result, index) => ({
      claim: claims[index],
      verified: result.confidence > 0.8,
      confidence: result.confidence,
      sources: result.sources
    }))
  };
  
  return validatedOutput;
}

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Training Data Verification

Verify training data against real-world sources: 
async function verifyTrainingData(dataset) {
  const verificationResults = await Promise.all(
    dataset.map(item => verifyDataItem(item))
  );
  
  const verified = verificationResults.filter(r => r.verified);
  const unverified = verificationResults.filter(r => !r.verified);
  
  return {
    total: dataset.length,
    verified: verified.length,
    unverified: unverified.length,
    accuracy: verified.length / dataset.length,
    issues: unverified
  };
}

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Historical Fact Validation with Archive

Validate historical claims using web archive: 
async function validateHistoricalFact(claim, date) {
  // Search archive for historical data
  const archiveResults = await searchArchive(claim.entity, date);
  
  // Compare with claim
  const matches = archiveResults.filter(result => 
    result.text.includes(claim.text)
  );
  
  return {
    claim,
    date,
    verified: matches.length > 0,
    confidence: matches.length / archiveResults.length,
    sources: matches
  };
}

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Multi-Source Cross-Referencing

Cross-reference facts across multiple sources: 
async function crossReferenceFact(fact) {
  const sources = await Promise.all([
    searchSERP(fact.query),
    searchDeepLookup(fact.entity),
    searchArchive(fact.entity, fact.date),
    searchSite(fact.url)
  ]);
  
  // Find common findings across sources
  const commonFindings = findCommonFindings(sources);
  
  return {
    fact,
    sources: sources.length,
    commonFindings,
    confidence: commonFindings.length / sources.length,
    validated: commonFindings.length >= sources.length * 0.7
  };
}

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Continuous Evaluation Systems

Build continuous evaluation systems for ongoing model validation:

Real-Time Monitoring

Monitor model outputs in real-time for continuous validation

Automated Testing

Automate fact-checking workflows for continuous evaluation

Alert Systems

Set up alerts for unverified claims or low confidence scores

Performance Tracking

Track evaluation performance and model accuracy over time

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Templates

Use pre-built templates for common grounding workflows:

Fact-Checking Template

Template for real-time fact-checking workflows

Model Evaluation Template

Template for comprehensive model evaluation

Training Data Validation

Template for validating training datasets

Historical Validation

Template for historical fact validation

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Next Steps

SERP API Quickstart

Start fact-checking with real-time search results

Deep Lookup Quickstart

Use Deep Lookup for comprehensive fact validation

Web Archive

Access historical data for fact-checking

Browse Examples

Explore grounding and evaluation examples
Need help? Check out our Evaluation Examples or contact support.