Hallucination Management

Managing Hallucinations in AI Agents

Hallucinations - instances where AI models generate incorrect information with high confidence - represent a significant challenge for AI agent development. At VrealSoft, we’ve implemented several strategies to minimize this issue.

Grounding Techniques

Knowledge Retrieval

We implement robust RAG systems that retrieve relevant, authoritative information before generating responses.

Source Attribution

Our agents are designed to cite sources and clearly indicate when information is derived vs. inferred.

Verification Systems

# Example of a verification system for fact-checking
def verify_response(self, response, query):
    # Extract factual claims
    claims = self.claim_extractor(response)

    # Check each claim against trusted sources
    verification_results = []
    for claim in claims:
        evidence = self.evidence_retriever.search(claim)
        confidence = self.claim_validator(claim, evidence)
        verification_results.append((claim, confidence))

    # Revise response if necessary
    if any(conf < self.confidence_threshold for _, conf in verification_results):
        return self.revise_response(response, verification_results)

    return response

Tip

Implementing a “verification loop” where the agent checks its own outputs can reduce hallucination rates by 30-40% in complex domains.

Multi-step Reasoning Workflows

We’ve found that breaking down complex reasoning into explicit steps helps reduce hallucinations:

1. Gather Information: Collect relevant facts from trusted sources 2. Reason Explicitly: Clearly articulate reasoning steps 3. Generate Alternatives: Consider multiple possible answers 4. Validate Conclusions: Check reasoning against available evidence 5. Express Uncertainty: Clearly communicate confidence levels

Guardrails and Safety Layers

Scope Boundaries

Clearly defining what the agent should and shouldn’t attempt to answer

Confidence Thresholds

Only providing definitive answers when confidence exceeds set thresholds

Implementation Example

Agent Configuration

agent_config = {
    "hallucination_controls": {
        "retrieval_sources": ["company_docs", "product_db", "verified_knowledge_base"],
        "confidence_threshold": 0.85,
        "uncertainty_communication": True,
        "fact_checking_enabled": True,
        "source_citation_required": True
    }
}

Response Generation

def generate_response(self, query):
    # 1. Retrieve relevant information
    context = self.knowledge_retriever.get_relevant_context(query)

    # 2. Generate candidate response with reasoning
    candidate = self.llm.generate(query, context)

    # 3. Verify factual accuracy
    verified_response = self.fact_checker.verify(candidate)

    # 4. Add appropriate uncertainty markers
    final_response = self.uncertainty_handler.process(verified_response)

    return final_response

Measurements and Evaluation

We track hallucination rates through:

• Regular human evaluation of agent outputs
• Automated fact-checking against trusted sources
• Comparison of agent responses with ground truth data
• User feedback collection and analysis

Note

Our hallucination reduction techniques have decreased incorrect information in agent responses by 65% compared to baseline models.

Future Improvements

We’re actively working on:

• Self-consistency checking across multiple reasoning paths
• Improved attribution and citation systems
• Domain-specific knowledge base expansion
• Enhanced uncertainty quantification