Why does AI sometimes give me different answers from different sources? Understanding how it picks between conflicting info

This is a fundamental challenge in AI systems. Here’s how conflict resolution typically works:

The evaluation hierarchy:

When conflicts arise, AI systems use:

1. Context-aware analysis - Examining the broader context of each claim
2. Data aggregation - Looking for patterns across multiple sources
3. Probabilistic reasoning - Sometimes presenting odds rather than definitive answers
4. Transparency mechanisms - Acknowledging when sources disagree

Key insight: AI doesn’t have a simple “truth detector.” It uses heuristics based on authority signals. Your content needs to demonstrate trustworthiness through these signals.

From my work in fact-checking, here’s what makes content win in conflicts:

Winning factors:

1. Primary source citations - Don’t just cite another article; cite the original research, data source, or official statement

2. Specific attribution - “According to [Organization] in their [Date] report” beats “Studies show…”

3. Methodology transparency - If you’re making claims, show how you arrived at them

4. Update acknowledgment - “As of [Date], the current guidance is…” signals awareness of changes

Example transformation:

Weak: “Most businesses see ROI from AI investments.”

Strong: “According to McKinsey’s December 2025 AI Report, 67% of enterprises reported positive ROI on AI investments within 18 months of deployment.”

The strong version gives AI systems specific, verifiable information to trust.

We’ve tested this systematically. Here’s our data:

Conflict resolution testing (200 query pairs):

The compound effect: When we had multiple winning factors, our win rate was 89%.

Strategy we now use: Every factual claim includes:

• The specific data point
• The source (organization/publication)
• The date of the source
• A link to the original

This “citation package” approach has dramatically improved our conflict win rate.

The primary source citation point is huge. We often cite secondary sources (news articles, blog posts) rather than the original research.

Question: What about when our accurate content conflicts with older but more authoritative sources? The older source might be wrong but has more trust signals.

Great question. This is the “authority vs. accuracy” tension.

Strategies to overcome older authoritative but outdated content:

1. Explicit supersession - Write content that explicitly states it updates/corrects older information. “While the widely-cited 2023 study found X, more recent research in 2025 shows Y because of Z.”

2. Build rapid authority - Get your updated content cited by other authoritative sources quickly. The citation network adjusts.

3. Leverage real-time platforms - Perplexity and similar real-time systems weight recency more heavily than training-data-based systems.

4. Create the definitive update - Don’t just have new data; create comprehensive content that becomes the new go-to resource.

The recency signal: AI systems increasingly recognize that information can become outdated. Using explicit date signals and update markers helps them understand your content represents the current state of knowledge.

Schema markup helps:

{
  "@type": "Article",
  "datePublished": "2025-01-01",
  "dateModified": "2026-01-05"
}

This tells AI systems explicitly when your content was updated.

In healthcare, this is life-or-death important. Here’s what we do:

Medical content conflict resolution:

1. Clinical review dates - “Medically reviewed by [Credentials] on [Date]”

2. Guideline tracking - Reference the specific clinical guidelines and their version

3. Update logs - Show when and why content was updated

4. Conflict acknowledgment - If guidance has changed, explicitly state the old vs. new recommendation

Our format:

Current recommendation (January 2026): [Recommendation]

Note: This supersedes the previous guideline from [Date] which recommended [Old approach]. The change reflects [Reason/New evidence].

This explicit framing helps AI systems understand the relationship between conflicting information.

Result: Our medically-reviewed content wins conflicts against older, higher-authority health sources about 75% of the time when we use this approach.

One thing that helps: uncertainty acknowledgment.

When AI systems see that you acknowledge uncertainty or conflicting evidence appropriately, it signals intellectual honesty that builds trust.

Examples:

• “While some studies suggest X, the evidence is mixed with Y also showing…”
• “Based on available data as of [Date], we recommend Z, though this may evolve…”
• “There’s debate among experts about A vs. B. The current consensus favors A because…”

This is counterintuitive - you’d think being definitive is better. But AI systems trained on high-quality sources recognize that good sources acknowledge complexity.

Where this matters most:

• Emerging topics where research is evolving
• Topics with legitimate expert disagreement
• Complex issues with multiple valid perspectives

Don’t oversimplify when appropriate nuance is needed.

Monitoring is essential for understanding your conflict win rates.

How we track this:

1. Identify queries where our content should be cited
2. Check if we’re actually being cited
3. When we’re not, analyze what IS being cited
4. Compare our content to the cited source
5. Identify specific gaps and fix them

Tools that help:

• Am I Cited for tracking citations across platforms
• Manual testing for specific conflict scenarios
• Competitive analysis to understand what wins

What we’ve learned:

• Conflicts are often on specific data points, not whole articles
• Fixing the specific conflicting claim often flips the citation
• Sometimes the issue is format/structure, not accuracy

This thread has been incredibly valuable. Summary of my action items:

Content changes:

• Always cite primary sources, not secondary articles
• Include specific attribution with dates
• Use explicit update/supersession language when appropriate
• Acknowledge uncertainty where it exists

Technical implementation:

• Add dateModified schema to all pages
• Create clinical-style review dates for expert content
• Build update logs for important pages

Monitoring:

• Track conflict scenarios with Am I Cited
• Identify where we’re losing conflicts
• Fix specific gaps rather than general optimization

Thanks everyone for the insights!