How Attanix Prevents Hallucination and Drift in High-Context AI Agents

Hallucination and context drift are two major challenges in AI systems. This article explores how Attanix's memory system addresses these issues through structured memory, context preservation, and salience-based retrieval.

Understanding the Problems

1. Hallucination

AI systems sometimes generate false or misleading information:

# Example of potential hallucination
query = "What were the key findings of the 2023 AI Safety Summit?"
response = "The summit concluded that AGI will be achieved by 2025"  # False information

2. Context Drift

Systems can lose track of conversation context:

# Example of context drift
conversation = [
    "User: Tell me about climate change",
    "AI: Climate change refers to long-term shifts...",
    "User: What about its impact on agriculture?",
    "AI: Agriculture is the practice of farming..."  # Lost context
]

How Attanix Addresses These Issues

1. Structured Memory Storage

# Structured memory example
memory_structure = {
    "facts": {
        "source": "reliable_database",
        "verification": "cross_referenced",
        "timestamp": "2024-04-12",
        "confidence": 0.95
    },
    "context": {
        "current_topic": "climate_change",
        "subtopics": ["agriculture", "impact"],
        "conversation_history": ["previous_messages"]
    }
}

2. Context Preservation

# Context preservation example
async def preserve_context(conversation):
    # Store conversation state
    await memory.store(
        content=conversation,
        context={
            "topic": extract_topic(conversation),
            "subtopics": extract_subtopics(conversation),
            "relationships": extract_relationships(conversation)
        }
    )
    
    # Maintain context chain
    await memory.link_context(
        previous=conversation[-2],
        current=conversation[-1]
    )

3. Fact Verification

# Fact verification example
async def verify_fact(statement):
    # Check against known facts
    known_facts = await memory.retrieve(
        query=statement,
        filters={"type": "fact"}
    )
    
    # Verify sources
    sources = await memory.retrieve(
        query=statement,
        filters={"type": "source"}
    )
    
    # Calculate confidence
    confidence = await calculate_confidence(
        statement=statement,
        facts=known_facts,
        sources=sources
    )
    
    return confidence > 0.8

Implementation Strategies

1. Memory Anchoring

# Memory anchoring example
async def anchor_memory(content):
    # Store with verification
    await memory.store(
        content=content,
        metadata={
            "verified": True,
            "source": "reliable_source",
            "timestamp": datetime.now()
        }
    )
    
    # Create relationships
    await memory.create_relationships(
        content=content,
        related_content=await find_related_content(content)
    )

2. Context Tracking

# Context tracking example
async def track_context(conversation):
    # Maintain conversation state
    state = {
        "current_topic": await extract_topic(conversation[-1]),
        "previous_topics": await extract_previous_topics(conversation),
        "relationships": await extract_relationships(conversation)
    }
    
    # Update context chain
    await memory.update_context_chain(state)
    
    # Check for drift
    drift = await detect_context_drift(state)
    if drift:
        await correct_context_drift(state)

3. Hallucination Prevention

# Hallucination prevention example
async def prevent_hallucination(response):
    # Verify facts
    facts = await extract_facts(response)
    for fact in facts:
        if not await verify_fact(fact):
            await correct_fact(fact)
    
    # Check consistency
    consistency = await check_consistency(response)
    if consistency < 0.8:
        await improve_consistency(response)
    
    # Validate context
    context_match = await validate_context(response)
    if context_match < 0.7:
        await adjust_context(response)

Best Practices

1. Memory Management

• Regular fact verification
• Context chain maintenance
• Relationship updates
• Confidence scoring

2. Context Preservation

• Topic tracking
• Subtopic management
• Relationship mapping
• State maintenance

3. Quality Control

• Fact checking
• Source verification
• Consistency monitoring
• Context validation

Real-World Examples

1. Customer Support

# Customer support example
async def handle_support_query(query):
    # Get customer history
    history = await memory.retrieve_customer_history()
    
    # Verify information
    facts = await verify_support_facts(query)
    
    # Maintain context
    context = await preserve_support_context(query)
    
    # Generate response
    response = await generate_verified_response(
        query=query,
        history=history,
        facts=facts,
        context=context
    )

2. Research Assistance

# Research assistance example
async def assist_research(query):
    # Get relevant papers
    papers = await memory.retrieve_relevant_papers()
    
    # Verify findings
    findings = await verify_research_findings(papers)
    
    # Maintain research context
    context = await preserve_research_context(query)
    
    # Generate summary
    summary = await generate_verified_summary(
        papers=papers,
        findings=findings,
        context=context
    )

3. Educational Tools

# Educational tool example
async def explain_concept(concept):
    # Get verified explanations
    explanations = await memory.retrieve_verified_explanations()
    
    # Check understanding
    understanding = await verify_understanding(concept)
    
    # Maintain educational context
    context = await preserve_educational_context(concept)
    
    # Generate explanation
    explanation = await generate_verified_explanation(
        concept=concept,
        explanations=explanations,
        understanding=understanding,
        context=context
    )

Next Steps

Ready to prevent hallucination and drift in your AI systems? Check out our documentation or try our safety guide.