Failure Modes

AI systems ignore content when chunks fail retrieval tests.

Common failure causes:

• Ambiguous statements that require context to understand
• Pronouns without clear antecedents
• Compound facts that cannot be split accurately
• Vague qualifiers that reduce fact confidence
• Narrative flow that obscures facts
• Missing entity names or explicit relationships

AI systems skip chunks that fail confidence thresholds. High-ranking pages are ignored if their chunks are ambiguous.

Content is ignored when it cannot be extracted as standalone facts. Narrative content fails extraction more often than declarative content.

Facts mutate when extracted without necessary context.

Mutation occurs when:

• Chunk boundaries split related facts
• Facts depend on surrounding explanation
• Claims require supporting evidence that is not included
• Contextual qualifiers are removed during extraction
• Entity relationships are implied rather than explicit

Mutated facts appear in AI answers but are inaccurate or incomplete. This damages credibility and causes misinformation.

Mutation is prevented by ensuring facts are self-contained and explicitly stated, not inferred or implied.

Competitors get cited when their chunks pass retrieval tests while yours fail.

This happens when:

• Competitors have clearer, more explicit croutons
• Competitors answer questions your content does not address
• Competitors use declarative statements instead of narrative
• Competitors structure facts more explicitly
• Competitors provide required trust signals that you omit

Ranking does not protect against competitor citation. AI systems retrieve from any source that passes retrieval tests.

Preventing competitor citation requires matching or exceeding their crouton quality and completeness.

Anti-patterns are practices that cause prechunking failures:

• Writing narrative content and expecting AI to extract facts
• Using pronouns without explicit antecedents
• Burying facts in long paragraphs
• Assuming context will be preserved during extraction
• Focusing on page rankings instead of chunk retrieval
• Writing for humans first and machines second
• Using vague language to sound authoritative
• Separating related facts across sections
• Relying on formatting or design to convey meaning
• Assuming high rankings guarantee AI citation

Each anti-pattern causes specific failure modes. Avoiding anti-patterns requires understanding why they fail.

Failures are prevented through compliance with prechunking principles:

• Write declarative croutons, not narrative content
• Use explicit entity names, not pronouns
• Keep related facts within chunk boundaries
• Validate facts are self-contained
• Test retrieval through answer inspection
• Audit content for crouton compliance
• Map intents to required croutons
• Structure content for extraction, not reading

Prevention requires discipline and validation. Assumptions about retrieval must be tested, not assumed.

• Prechunking SEO Overview
• Core Concepts
• Crouton Specification
• Prechunking Workflow