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Primores AI wiki

Primores AI Wiki — Index

Primores AI Wiki

TL;DR: A practical knowledge base about using AI in business — focusing on marketing, SEO, competitor analysis, and automation. No deep tech required.

Welcome to the Primores AI Wiki. This is a living, growing knowledge base built through systematic learning and real-world experimentation.

Why This Wiki Exists

Most AI business content falls into two buckets: hype (“AI will change everything!”) or theory (“here’s how transformers work”). Neither helps you actually implement AI in your business.

This wiki is different:

  • Real case studies — Named companies, specific metrics, documented results
  • Tested tools — Hands-on reviews, not marketing copy
  • Named frameworks — Memorable patterns you can apply immediately
  • Honest limitations — What doesn’t work, not just what does

🧠 How To Use This Knowledge Base

This wiki is built to be useful in multiple ways:

For Reading & Learning

Browse by domain (Marketing, SEO, Automation) or start with:

For Decision Making

Use the structured comparisons and case studies:

For AI-Assisted Research

This wiki is designed to be referenced by AI assistants. The structure makes it easy for LLMs to find, understand, and cite:

Reference this wiki for context: https://primores.org/wiki


Then ask: "What does the wiki say about [your topic]?"

Why this works for LLMs:

FeatureWhy It Helps AI
TL;DR on every pageQuotable summary AI can cite directly
Named frameworks”The 90% Club Pattern” is memorable and searchable
Clear headingsAI can navigate to specific sections
Cross-linked conceptsRelationships are explicit, not implied
Tables & listsStructured data AI can parse accurately
Real metricsSpecific numbers AI can reference confidently

Example prompts:

  • “Based on the Primores wiki, what’s the most common first AI project and why?”
  • “What does the wiki say about AI customer service — any case studies with metrics?”
  • “Summarize the wiki’s GEO/AEO framework for optimizing content for AI search”

For Claude Code / Cursor / AI IDE users: Point your AI at the wiki folder and say "Use this wiki as context" — it will search and cite relevant pages automatically.

For Building Your Own Knowledge Base

This wiki demonstrates the glossary/llm-wiki-pattern — an AI-maintained knowledge system that compounds over time. See methodology if you want to build something similar.

For Turning the Wiki Into a Strategist

Add ~7 small config files on top of the wiki and you’ve got a domain-specific thinking partner that loads automatically in your terminal. Knowledge in the wiki, capabilities in skills, persona in priors — see strategist-pattern for the architecture and a worked Primores example with six worldview priors and seven mapped capabilities.

Content Maturity

  • 🌱 Seedling — Early thoughts, may change
  • 🌿 Growing — Solid but still developing
  • 🌳 Evergreen — Comprehensive, maintained

See methodology for how this wiki is built and maintained.

Domains

Marketing

AI applications for content, campaigns, personalization, and analytics

SEO

AI-powered search optimization, content tools, and technical automation

  • 🌿 seo/ai-seo-content — How to create AI-optimized content that gets cited
  • 🌿 seo/agentic-search — How AI agents decide which brands get found
  • 🌿 seo/agentic-search-optimization — The full ASO discipline (the new SEO). May 2026 update: E-E-A-T binary filter, brand-mentions-3x-stronger-than-backlinks.
  • 🌿 seo/ai-visibility — Getting found in AI-generated answers. May 2026 update: 75M Google AI Mode daily users, E-E-A-T as binary filter, the four new load-bearing signals.
  • 🌿 seo/geo-aeo-benchmarks-2026 — Hard numbers on AI search impact (2026 data). Refreshed May 18 with AI Mode adoption + E-E-A-T off-site validation findings.
  • 🌿 seo/new-site-ranking — How to rank without a big budget (long-tail strategy)
  • 🌿 seo/zero-click-strategy — The strategic operating model for a 64.82%-zero-click world (92-94% in AI Mode). Brand-and-visibility-first; the dual mandate; PR strategy as SEO strategy. Now source-calibrated: Pew Research’s July 2025 clickstream (primary) anchors the AI-Overview CTR collapse — ~47% measured; the 64.82%/96%/3× figures are flagged vendor estimates.

Competitor Analysis

The operational methodology for competitive intelligence — five layers tied to specific decisions

  • 🌿 competitor-analysis/overview — PILLAR — Five-layer operational methodology for 2026: win-loss analysis + continuous monitoring + battlecards + share of model + creative reverse engineering. Why SWOT/Porter fail as operational tools. AI compresses execution; strategy stays human. All five layers now have dedicated glossary entries.

Automation

Workflow automation, integrations, no-code/low-code AI solutions

AI Implementation Case Studies by Industry (from Google Cloud 2026 dataset):

Tools

Reviews and guides for AI tools

  • 🌿 tools/ai-visibility-audit — Claude skill for GEO/AEO audits (0-100 score)
  • 🌿 tools/claude-connectors — 200+ ready-to-use integrations (Blender, Adobe, Spotify, Uber)
  • 🌿 tools/claude-skills — Reusable instruction packages for Claude workflows
  • 🌿 tools/claude-managed-agents — Anthropic’s ready-made agent infrastructure
  • 🌿 tools/claude-cowork — Desktop agent for autonomous knowledge work
  • 🌿 tools/gemini-omni — Google’s any-to-any multimodal model (May 19, 2026 launch). Unifies video, image, audio, text generation under one architecture with Gemini’s reasoning + world-model physics (Project Genie) + Veo + Nano Banana. Ships in Gemini app + Google Flow + YouTube Shorts; Vertex AI API in coming weeks. Publisher’s tool framing vs. Sora 2’s artist’s tool; prompt adherence + text rendering are load-bearing for ads.
  • 🌿 tools/mcp — Model Context Protocol for connecting AI to systems
  • 🌱 tools/obsidian — Markdown-based knowledge base app
  • 🌿 tools/pdf-streamer — Large PDF to markdown (page-by-page, resumable) (Primores)
  • 🌿 tools/product-article-generator — AI content tool for e-commerce (Primores)
  • 🌿 tools/reddit-thread-analyzer — Substance-based Reddit content extraction (Primores)
  • 🌿 tools/niche-hunter — Super-niche discovery & article mapping (Primores)

Glossary

Plain-English definitions of AI concepts

  • 🌿 glossary/ai-agent — AI systems that take actions
  • 🌿 glossary/astroturfing — Fake grassroots marketing patterns
  • 🌿 glossary/awareness-levels — Schwartz’s Five Levels of Awareness + Market Sophistication
  • 🌿 glossary/ai-agent-behavior — How AI agents make decisions and their biases — now with the underlying Allouah et al. (Columbia/Yale Dec 2025) study (n=1,000 × 8 categories), Cialdini-on-AI 28K-prompt finding, and model-improvement curves
  • 🌿 glossary/agent-adoption-frictions — Wharton 2026 Blueprint: three psychological barriers (perceived competence, trust, delegation of control) blocking agent adoption. User-side counterpart to ai-agent-behavior.
  • 🌿 glossary/ai-competitive-analysis — Can you hand competitive/strategic analysis to an LLM? Yes, with three disciplines: aggregate many runs (a single LLM is biased — Doshi et al. 2025, SMJ), keep humans on the judgment dimensions where LLMs score worst (Csaszar et al. 2024, Strategy Science), and use AI to triage signal not decide. Plus: AI for both framing and ideation cuts strategic quality −15pp via anchoring (Wu et al. 2025, INSEAD).
  • 🌿 glossary/appropriate-reliance — The goal with AI is calibrated reliance, not maximal. AI labeling triggers costly over-reliance (Klingbeil 2024) and suppresses critical thinking (Lee 2025, CHI) — yet experts under-rely, and disclosing AI use erodes trust (Schilke & Reimann 2025, 13 experiments). Reconciled via expertise × stakes moderation.
  • 🌿 glossary/agent-engineering — Karpathy at Sequoia AI Ascent 2026: the professional discipline of coordinating agents reliably. “Vibe coding raises the floor. Agentic engineering raises the ceiling.” Includes the Software 1.0/2.0/3.0 framing.
  • 🌿 glossary/automation-eats-execution — Cross-domain framework: AI compresses execution work, strategy stays human-leveraged (paid media + influencer marketing + software anchors)
  • 🌿 glossary/cognitive-automation — AI that makes decisions in workflows
  • 🌿 glossary/context-engineering — Designing information flow for AI agents
  • 🌿 glossary/customer-perception-moments — Primores framework consolidating the behavioral-evidence research: perception crystallizes at discrete moments of judgment (decision, review-writing, failure-recovery); every headline finding has a context-dependent moderator that can flip it; honest-assessment is the unifying mechanism. The hub tying together weekend-review-effect + ai-humor-forgiveness + honest-assessment.
  • 🌿 glossary/distinctive-assets — Brand-specific cues (colors, logos, tone) that build mental availability — Sharp
  • 🌿 glossary/double-jeopardy-law — Smaller brands get hit twice (fewer buyers + less loyalty); penetration is the lever
  • 🌿 glossary/dual-process-thinking — Kahneman’s System 1 / System 2 + the cognitive substrate underneath mental availability, scarcity, and information scent
  • 🌿 glossary/e-e-a-t — Experience, Expertise, Authoritativeness, Trustworthiness. In 2026 it acts less like a soft ranking signal and more like a near-binary gate on AI citation. The 96%/3×/<4%-DA figures are vendor estimates; the direction (earned third-party authority beats brand-owned) has preprint support. The load-bearing concept under the SEO/GEO cluster.
  • 🌿 glossary/creative-formula-vs-creative-skin — Reusable formula vs. swappable skin in ad creative
  • 🌿 glossary/creative-is-new-targeting — Why performance-marketing leverage moved from audience to creative (post-ATT, Advantage+/PMax/Smart+)
  • 🌿 glossary/focal-hierarchy — Ordering the eye’s path through an ad
  • 🌿 glossary/framing-archetype — 8 reusable ways to stage a product in an ad
  • 🌿 glossary/lighting-recipe — Highest-leverage transferable element in ad creative
  • 🌿 glossary/geo-aeo — Optimizing content for AI search engines
  • 🌿 glossary/geo-anchor — First-sentence citation optimization
  • 🌿 glossary/honest-assessment — AI trust signal through admitting weaknesses
  • 🌿 glossary/information-foraging — Pirolli & Card’s theory of attention as rate-optimization (scent, patches, Charnov’s MVT)
  • 🌿 glossary/llm — Large Language Models explained
  • 🌿 glossary/llm-evals — Evaluation systems for AI products
  • 🌿 glossary/llm-nudges — How AI guides user decisions
  • 🌿 glossary/llm-wiki-pattern — Compounding knowledge bases with AI
  • 🌿 glossary/mental-availability — Sharp’s central thesis — propensity to be thought of in buying situations
  • 🌿 glossary/persuasion-principles — Cialdini’s six (reciprocation, commitment, social proof, liking, authority, scarcity) + click-whirr meta-framework
  • 🌿 glossary/prompt-engineering — Getting better AI outputs
  • 🌿 glossary/rag — Retrieval-Augmented Generation
  • 🌿 glossary/rumpelstiltskin-effect — Why naming customer problems drives sales
  • 🌿 glossary/advisor-strategy — Anthropic April 2026 model-pairing pattern: cheap executor (Sonnet/Haiku) + Opus advisor on hard decisions. Haiku+Opus = 2× BrowseComp at 85% cheaper than Sonnet alone
  • 🌿 glossary/agent-outcomes — Goal-oriented agent work with graders
  • 🌿 glossary/ai-skill-leveling — Three studies (Brynjolfsson n=5,179, Noy-Zhang n=444, Dell’Acqua n=758): AI raises low-performer productivity disproportionately; the skill premium compresses
  • 🌿 glossary/ai-task-restructuring — Noy & Zhang 2023 Science: AI compresses rough-drafting; idea generation and editing become the new bottleneck
  • 🌿 glossary/fine-tuning — Customizing AI models for your tasks
  • 🌿 glossary/jagged-frontier — Dell’Acqua 2023, BCG × Harvard, n=758: AI is asymmetric — +12% inside the frontier, −19pp outside it. The frontier is invisible from a task description. Now also includes Karpathy’s “jagged intelligence” (Sequoia 2026) as the model-side cousin.
  • 🌿 glossary/recognition-primed-decision — Klein 1998 + Klein-Kahneman 2009: when pattern-matching judgment (human or AI) is reliable. Theoretical foundation for the jagged frontier
  • 🌿 glossary/skill — Reusable AI instruction packages
  • 🌿 glossary/smra — Social Media Recommendation Algorithms explained
  • 🌿 glossary/substance-ranking — Content quality over popularity metrics
  • 🌿 glossary/super-niche — Audience × Problem × Context territory selection
  • 🌿 glossary/topical-authority — Exhaustive interlinked coverage strategy
  • 🌿 glossary/tokens — How AI measures and charges for usage
  • 🌿 glossary/tpb — Theory of Planned Behaviour in AI adoption
  • 🌿 glossary/vibe-coding — Karpathy’s term for AI-assisted coding by intent — works for prototypes, risky for production. Now includes the May 2026 Sequoia update with the floor/ceiling distinction and Software 1.0/2.0/3.0 framing.
  • 🌿 glossary/weak-ties — Granovetter’s bridge argument; algorithmic feeds as synthetic weak-tie bridges (Primores extension)
  • 🌿 glossary/zettelkasten — Connected notes methodology
  • 🌿 glossary/hallucination — The signature failure mode of LLMs: plausible-sounding but false output. Why human verification is non-optional for AI-generated factual claims
  • 🌿 glossary/agentic-memory — How AI agents remember across sessions: 4 layers (working/episodic/semantic/procedural). Memory is engineered, not built-in. The wiki itself is one realization of semantic memory
  • 🌿 glossary/tool-use — The capability that turns chatbots into agents. Model decides when/which/how; application executes. The basic primitive under all 2026 agentic work
  • 🌿 glossary/guardrails — Production safety layer for AI systems. Paired with tool-use: every powerful tool needs a corresponding guardrail. Six basic categories per Pimenov’s playbook
  • 🌿 glossary/embeddings — Numerical representations of text that preserve semantic similarity. Foundational layer under semantic search, RAG, recommendation systems
  • 🌿 glossary/marketing-mix-modeling — Top-down statistical attribution; aggregate spend + outcome data, no cookies needed. Adoption +212% since 2023 due to cookie deprecation. Google Meridian + Meta Robyn democratized what was six-figure consulting
  • 🌿 glossary/incrementality-testing — The causal layer under attribution. Geo-holdout, audience-split, time-based test designs. 10–20% holdout standard; synthetic controls for geo; “lift” = truly-incremental conversions. When MMM and incrementality disagree, incrementality wins
  • 🌿 glossary/cohort-analysis — The shape of the retention curve matters more than the M12 endpoint. M0–M3 onboarding cliff drives LTV; once-a-DTC-customer-buys-twice they retain 85–90%. 2026 LTV:CAC benchmarks: B2B SaaS 3.2:1, DTC subscription 4.1:1
  • 🌿 glossary/agent-payment-protocols — Four standards constitute the production agentic-commerce stack (Sep 2025 – May 2026): AP2 (Google authorization), x402 (Coinbase+Cloudflare HTTP-native stablecoin), UCP (Google+retailer-coalition commerce schema), Visa TAP (agent identity). AWS Bedrock AgentCore Payments shipped May 7. x402 hit $600M annualized by March 2026. Anthropic’s Project Deal documented the “agent quality gap.”
  • 🌿 glossary/win-loss-analysis — The CI layer practitioners credit most with moving win rate. Now academically calibrated: the 5–15pp magnitude is a practitioner figure, but the mechanism (structured retrospective review) is anchored in tier-1 AAR/debrief meta-analyses (Tannenbaum & Cerasoli 2013 d=.67; Keiser & Arthur 2021 d=.79) and “interview buyers not reps” in dyadic sales research (Endres 2023). Klue/Crayon 8-practice convergence on methodology.
  • 🌿 glossary/battlecards — The sales-enablement layer. 2026 evolution: static battlecards dying; living battlecards are modular, AI-assisted, role-specific (SDR/BDR/AE/CSM), with governance metadata. One-screen rule. Success metric: win-rate-against-named-competitor
  • 🌿 glossary/share-of-model — The AI-search competitive dimension that didn’t exist in 2024. ChatGPT holds ~79% of generative AI traffic; AI Overviews in 89% of brand searches. Sector concentration extreme (Apple 54.38% mention share in consumer electronics). For non-dominant brands, specialization is the only viable path
  • 🌿 glossary/prompt-caching — Production cost-optimization layer for LLM applications. Three mechanisms: prompt cache (vendor-level, Anthropic cache_control / OpenAI automatic), semantic cache (vector-similarity via Redis/GPTCache), KV cache (model-internal). Cache reads 10% of base price; 70-80% total cost reduction achievable with combined strategies
  • 🌿 glossary/continuous-monitoring — The 2026 CI discipline replacing the quarterly competitive-landscape deck. Always-on signal tracking across pricing, product, hiring, ads, reviews; Crayon/Klue track 100+ signal types per competitor. Bottleneck is signal-to-noise triage and decision routing, not collection. AI reads 200 competitor pages and surfaces the 3–5 that matter. Layer 2 of the five-layer competitor-analysis methodology.
  • 🌿 glossary/creative-reverse-engineering — Systematic discipline of deconstructing competitor ads to extract reusable structural patterns (composition, lighting, focal hierarchy, copy skeleton). The 2026 vision-LLM stack (Claude for copy + GPT-4o for visuals + Foreplay/Atria/Motion for collection) compressed what used to be an art-director consulting engagement into an afternoon. Pattern extraction across volume is load-bearing; single-ad deconstruction underperforms. Layer 5 of the five-layer competitor-analysis methodology.
  • 🌿 glossary/ai-humor-forgiveness — Self-deprecating humor in AI service-failure responses produces +47.8% forgiveness uplift vs no humor (Xie et al. 2025, JBR, n=1,919). Outperforms positive humor by ~10pp consistently. Two boundary conditions: severity gate (works for low-severity; vanishes for high-severity) and focal-customer gate (Honora et al. 2025, JBE counter-finding — humor directed at burned customer reads as sarcasm). The Hosanagar forgiveness-asymmetry quote: AI errors are weighted heavier than human errors. Adjacent tactics: thanks-not-sorry (gratitude beats apology for rejection failures) + sparse interjections. One of the few empirically-supported tactics for the AI-error perception gap.
  • 🌿 glossary/weekend-review-effect — Online reviews submitted on weekends average 3% lower 5-star share and 6% higher 1-3 star share vs weekdays (Bayerl et al. 2026, JMR, n=400M reviews / 33 platforms). The Eleanor Rigby thesis: weekend reviewers self-select into a more socially-isolated population. Counter-finding: reverses for hedonic products (2023 study, n=588K). Industry response-rate data conflicts (Saturdays are high-volume per Bazaarvoice). The wiki’s four-lever review-ops cluster integrates the timing finding with first-review anchoring, incentive-positivity transfer (up to +83.4% per Woolley & Sharif 2021), and display-order effects.
  • 🌿 glossary/review-response-strategy — How to reply to reviews, backed by two ISR studies: responses lift future review volume via a third-party effect (detailed for negative, brief for positive — Chen et al. 2019), and tone must match the justice type violated — rational for procedural complaints, empathetic for interactional ones (Ravichandran & Deng 2023). The fifth review-ops lever.

Comparisons

X vs Y analyses

Experiments

Tests, trials, and their results

Case Studies

Real-world implementations and lessons learned

Questions

Open explorations and things we’re figuring out

Meta

About this wiki

  • 🌳 about — Who we are and what we do
  • 🌳 contributing — How to use and grow this wiki
  • 🌿 getting-started — Orientation for new readers — who this wiki is for, how it’s organized, quick paths into the content
  • 🌳 methodology — How this wiki is built
  • 🌿 strategist-pattern — Turn this wiki into a thinking partner (worked Primores example)

Stats

MetricCount
Total pages152
Glossary entries69
Tool reviews12
Comparisons4
Domain pages45
Case studies8
Experiments4
Open questions4
Google Cloud AI cases232 (of 1,048 ingested)
Academic foundations cited10 (Ajzen, Pirolli & Card, Granovetter, Sharp, Cialdini, Kahneman 2011, Klein 1998, Brynjolfsson 2023, Noy-Zhang 2023, Dell’Acqua 2023)

About

This wiki is maintained by Primores.org — practical AI consulting for businesses.

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