# Addressing Merge Conflicts in AIO Guide %%[[Bias and Knowledge Conflicts in Retrieval-Augmented Language Models (RALM)]]%%%Current LLMs perform well in identifying the existence of knowledge conflicts but struggle to determine the specific conflicting segments. This creates a clear opportunity for content that explicitly identifies and reconciles these conflicts, as the AI systems recognize the need but cannot generate the reconciliation themselves. In fact, AI systems are being specifically designed to handle inconsistent reporting from varying sources by identifying patterns and reconciling differences (eg. [Adding AI to Modern Treasury Reconciliation | Modern Treasury Journal](https://www.moderntreasury.com/journal/adding-ai-to-modern-treasury-reconciliation)), suggesting that content performing this function naturally would be highly valued by AI systems. This could be what Google is doing with AIO, if we look at their patent information. %%[[AIO Deep Dive]]%%Although patents don't necessarily reflect current implementation, [Google's Patents](https://patents.google.com/patent/US11769017B1/en) suggest that AI Overview isn't just pulling the highest-ranking pages for the original query + sub-queries and then generating a summary. It may be generating potential responses and then searching for sources that can credibly verify specific claims within those responses. This is indeed a form of Retrieval Augmented Generation (RAG), but whether the system prefers algorithmic fact-checking or semantic similarity to a Hypothetical Document Embedding (HyDE) is the key content filtering mechanism, no one can know for sure. Some misinterpret these dynamics by treating AI Overview optimization like a matching game—comparing chunked website content embeddings with embeddings of existing AIO citations to find semantic similarities. **This optimization mindset captures the correlation patterns but misses the underlying value mechanism.** --- The research reveals a fundamental tension in how LLMs navigate conflicting information: while they can detect when knowledge conflicts exist, they struggle to identify the specific conflicting segments and reconcile them effectively. This creates a systematic vulnerability where LLMs exhibit both confirmation bias (favoring their parametric knowledge when presented with mixed evidence) and context bias (overriding their correct prior knowledge ~60% of the time when given incorrect external information). The models' arbitration between sources depends heavily on their confidence levels—they're more receptive to external evidence when uncertain, yet become resistant to contradictory information when confident in their prior knowledge. Critically, current systems prioritize relevance over credibility indicators that humans value (like scientific references or neutral tone), and their preferences are swayed by superficial factors like source popularity, order, and quantity. This suggests that content creators who can explicitly identify, segment, and reconcile conflicting information—essentially performing the disambiguation that LLMs cannot—may gain significant AI visibility advantages, as these systems recognize the need for reconciliation but cannot generate it themselves. --- The research confirms that Google's AI Overview and similar systems aren't just summarizing top-ranking content—they're actively seeking sources that can resolve the conflicts and uncertainties inherent in their knowledge bases. superior content = reduces the cognitive load of reconciling conflicts, provides clear evidence hierarchies, and explicitly addresses contradictions that AI systems detect but cannot resolve. navigate informational complexity This explains why content addressing emerging topics or edge cases (where models have lower confidence) sees disproportionate AI visibility. The optimization mindset of matching semantic embeddings misses the point—AI systems seek content that can verify and support specific claims, not just topically similar content. Since models demonstrate "deficiency in information integration," content that pre-integrates evidence from multiple sources provides unique value. --- **Actually supported by the research:** 1. **Target Knowledge Gaps** - Yes, the ClashEval study explicitly found that "LLMs are more likely to accept external evidence when uncertain" 2. **Majority Rule** - Yes, the Tug-of-War paper directly states that "RALMs follow the principle of majority rule, leaning towards placing trust in evidence that appears more frequently" The most honest takeaway is that the research reveals AI systems are surprisingly malleable when presented with external information—but translating this into specific content tactics requires assumptions beyond what the studies directly demonstrate. The research best supports targeting **temporal knowledge gaps** (post-training events) rather than topical ones. For everything else, you're making reasonable but untested assumptions about where LLMs lack confidence. More conservative actionables might be: - **Emerging topics and product comparisons post-dating training cutoffs** - LLMs would have zero prior knowledge here, maximizing the "uncertainty" effect where they accept external evidence - Create multiple authoritative sources on the same topic (majority rule) - Monitor which topics trigger frequent search behavior in AI systems (indicating low confidence) Logically sound but not directly tested: - **Niche technical areas** and **Regional/local information** where training data was sparse, although the studies focused on factual conflicts rather than knowledge voids - **"Answer libraries"** - This leverages the "majority rule" finding (multiple sources saying the same thing), not the uncertainty principle --- Analysis the Guide against this new research: - Pack content with statistics, data points, and citations—not for credibility (which LLMs ignore) but for **relevance density**. The statistics make your content more relevant to queries, not more trustworthy. - Target topics where AI lacks confidence—emerging technologies, recent events, niche specializations—with frequently updated content. The recency bias compounds with uncertainty to maximize adoption. - **Create video + text versions of the same content. This isn't just about platform preferences—it's about increasing the frequency of your information appearing in retrieval sets, exploiting the majority rule principle across different content types.** - For B2B companies, publish technical specifications, methodologies, and proprietary insights directly on company domains. The LLM's tendency to trust retrieved content combines with B2B citation patterns to create outsized opportunity. The research does NOT support trying to override AI's strong prior knowledge on well-established topics—focus on areas of uncertainty instead. --- ### Dual Approaches to LLM-Based Summary Generation Google's patent "[Contextual suppression of assistant command(s)]([https://patents.google.com/patent/US11769017B1/en](https://patents.google.com/patent/US11769017B1/en))" (U.S. Patent No. 11,769,017 B1, 2023) describes two distinct architectures for generating AI-powered search summaries, revealing Google's strategic flexibility in implementing retrieval-augmented generation: 1. The **Search-First approach** (Method 200, Fig. 2) retrieves search result documents responsive to the query, selects a subset based on query-dependent and user-dependent measures, then processes "corresponding content from each of the search result documents of the set" through the LLM to generate the summary (Block 260). 2. In contrast, the **Generate-First approach** (Method 300, Fig. 3) allows the LLM to generate content "independent of any SRD(s) [search result documents]" based solely on the query (Sub-block 354B), then subsequently searches for documents to verify each portion of the generated content (Sub-block 358B), selectively linkifying only those portions that can be verified against retrieved sources. **This dual architecture addresses a fundamental challenge in neural Information Retrieval (IR):** - Search-first: ensures grounding on external knowledge sources but may miss valuable insights from the LLM's parametric knowledge - Generate-first: leverages the model's parametric knowledge but requires post-hoc verification to prevent errors. By patenting both approaches, Google preserves optionality to dynamically select the optimal method. --- The patent shows Google can dynamically switch between two fundamentally different approaches: - **Search-First** (what you assumed) → retrieves documents first, then processes them - In Google's **Generate-First mode**, the LLM draws from its parametric knowledge (training data) to generate content, then searches for sources to link to. This means brands with strong historical representation in training corpora (Common Crawl, news archives, Wikipedia, etc.) get mentioned in the initial generation phase, regardless of their current search rankings. **Your "7 Dimensions" Framework Needs Updating** - **System Architecture (#1)** should: - Acknowledge Google's dual-mode capability rather than categorizing it as purely "search-first." Add that established brands benefit from both current content quality AND historical training data presence. - Explain Google's patent-revealed flexibility: acknowledge Google's dual-mode capability rather than categorizing it as purely "search-first" - Explain how Generate-First mode creates "training corpus privilege" - Some aren't discovered through search but generated from training data, then verified. - Note that this explains some of the citation patterns that don't correlate with current rankings - this places varying emphasis on pre-existing brand mentions in training corpora - **Content Relevance (#4)** becomes more complex - content needs to work for both discovery-based citation (Search-First mode) AND verification-based citation (Generate-First mode). - Content should be optimized not just for query relevance, but also as authoritative verification sources. This strengthens your "coherence" dimension - content that pre-reconciles conflicts becomes valuable for verification. - **"Content Recency" (#6)** becomes more nuanced: - Fresh content matters for Search-First mode discovery - But Generate-First mode favors brands with deep historical presence in training corpora - This creates a "rich get richer" dynamic you don't currently address - **Your competitive dynamics analysis needs updating:** Your guide suggests that "lower-ranked sites can gain significant AI Visibility through superior content" - but Generate-First mode actually advantages established brands with historical training data presence, regardless of current content quality. - Acknowledge that while content quality matters, established brands have structural advantages from training corpus presence. Frame this as "level the playing field through superior current content" rather than "overtake through content alone." - **Professional vs Consumer Context (#3)** gets deeper: Your 4.25x B2B citation gap likely reflects more than user intent - **enterprise software companies historically generated more training-corpus-worthy content** (technical documentation, case studies, API docs) that now advantages them in Generate-First mode. Strategy implications: - **For established brands:** Don't neglect current content, but recognize you have parametric knowledge advantage from historical web presence - **Newer brands/companies**: Emphasize building broad, authoritative web presence beyond just optimizing individual pages AND build sufficient web presence to influence future training cycles - think "training data for future models" not just "current search optimization" - **B2B vs B2C patterns**: Your 4.25x citation gap for B2B might partially reflect that enterprise software companies have historically generated more training-corpus-worthy content (technical documentation, case studies, etc.) - Add that B2B companies benefit from both current content optimization AND accumulated technical content in training data. This insight actually makes your guide more valuable by explaining why some brands seem to "punch above their weight" in AI citations despite mediocre current content. --- Your current 3-category system misses Google's most sophisticated capability: **dynamic switching between Generate-First and Search-First approaches**. This isn't just about verification—it's about **when your brand gets considered in the process**. **Generate-First scenarios** favor brands with strong representation in LLM training data (Common Crawl, etc.) because the AI generates content mentioning them _first_, then searches for verification sources. **Search-First scenarios** rely purely on current search discoverability. **Actionable revision**: Reframe this section around "Training Data Advantage vs. Search Advantage" rather than static system types. Companies need to optimize for both scenarios since Google dynamically chooses the approach. Getting mentioned in LLM training data creates citation opportunities independent of search rankings. Focus on getting referenced in sources likely to be included in future training datasets. Add another step: **Training Data Positioning** - **Historical web presence** → Ensure your brand/expertise was well-represented in Common Crawl archives - **Wikipedia optimization** → Critical for training data inclusion across all major LLMs - **Academic citations** → Research papers and technical documentation often included in training sets Instead, recognize that comprehensive web presence serves both scenarios—historical content for training data representation, fresh content for search-based verification. Your current framing suggests you can predict which approach AI will use. The patent shows you can't. Since Google's system can dynamically switch approaches, optimize for both parametric knowledge representation (training data) and real-time discoverability (search results). This keeps your advice actionable while acknowledging the system's complexity without claiming to reverse-engineer it. --- But Google's approach adds critical complexity. Recent patent filings reveal that **Google's AI Overview can dynamically switch between two distinct processes**: - **Generate-First**: The LLM generates content using parametric knowledge, then searches for sources to verify specific claims - **Search-First**: The system searches for relevant documents first, then synthesizes content from retrieved sources