What Is Artificial Intelligence? Definition and Examples
Learn what artificial intelligence is, how it works, its main types, key milestones, everyday applications, benefits, and limitations.
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Notes from what we are building
Writing on AI, marketing data, and the work behind it. For people who have to make the call, not to chase keywords.
0112 articles
View category →AI Fundamentals
Start with what artificial intelligence means, how a model turns input into output, and where machine learning and deep learning fit. This cluster builds the vocabulary needed before exploring generative AI, agents, and analytics applications.
What Is Agentic AI? How It Works and How It Differs
Agentic AI can plan and take actions towards a goal. Learn how it works, what components it needs, and how it differs from traditional AI.
What Is Generative AI? How It Works and Real-World Uses
Generative AI creates text, images, audio, video, and code from learned patterns. Learn how it works, where it is used, and what can go wrong.
AI vs Machine Learning vs Deep Learning: Key Differences
Compare AI, machine learning, and deep learning by scope, methods, data requirements, computing needs, and practical use cases.
How Does AI Work? From Training Data to Output
Understand how AI works through data preparation, model training, inference, and output, with clear examples and practical limitations.
024 articles
View category →Generative AI & LLMs
Explore how generative models produce responses, retrieve external context, and still fail. The focus is technical enough to be useful without turning into model-development documentation.
How Generative AI and LLMs Work: Tokens, RAG, and Evaluation
Learn how generative AI and LLMs use tokens, context windows, Transformers, retrieval, RAG, and evaluation to produce and assess outputs in practice.
What Is a Large Language Model? How LLMs Work and Where They Fall Short
Learn what a large language model is, how an LLM works at a practical level, what it can do, and which limitations businesses need to understand.
What Is RAG? How Retrieval-Augmented Generation Works
Learn what retrieval-augmented generation is, how a RAG pipeline works, when it helps, and which risks still require evaluation.
Why Do Large Language Models Hallucinate? Causes, Risks, and Mitigation
Understand why LLMs hallucinate, how factual and groundedness failures differ, and which controls can reduce and evaluate the risk.
034 articles
View category →AI Agents & Intelligent Systems
AI agents do more than generate text. This cluster explains how models use tools, retain context, complete steps, and operate within limits that people can inspect and control.
AI Agent Architecture: Models, Tools, Memory, Workflows, and Guardrails
Learn how models, tools, memory, workflows, permissions, guardrails, human oversight, and evaluation together form a practical AI agent system.
Guardrails for Controlling AI Agent Actions
Learn how AI agent guardrails constrain capabilities, permissions, autonomy, data, execution, impact, and recovery when controls fail.
AI Agent vs Chatbot: What Is the Difference?
Compare AI agents and chatbots across goals, tools, autonomy, memory, actions, risks, and the business situations suited to each.
Human-in-the-Loop for AI Agent Systems
Understand human-in-the-loop for AI agents: approvals, escalation, review, approval fatigue, risk tiers, and effective oversight design.
044 articles
View category →AI for Marketing Analytics
This cluster examines how AI connects data, detects changes, and builds analytical context. The emphasis is not automation promises, but decision material that remains open to verification.
AI for Marketing Analytics: From Fragmented Data to Decisions
Learn how AI can connect marketing data, detect changes, build insights, and prepare evidence that supports accountable marketing decisions.
How AI Connects GA4, Search Console, and Google Ads
Learn how AI connects GA4, Search Console, and Google Ads without conflating each platform's metrics, attribution, or analytical role.
AI for Detecting Marketing Data Anomalies
Learn how AI detects marketing data anomalies, builds baselines, reduces false alerts, and turns unusual signals into investigations.
Data Readiness Before Using AI for Marketing Analytics
Prepare marketing data for AI through clear objectives, metric definitions, tracking, data quality, access, privacy, and validation.
0522 articles
View category →Search Visibility
Search visibility begins before a click occurs. This cluster explains how bots discover pages, search engines build indexes, algorithms select results, and AI systems compose answers and cite sources.
What Is SEO? How It Works and Why It Matters to Businesses
SEO helps search engines understand a website and connect it with relevant searches. Learn how it works, what it supports, and why it matters.
How to Analyse Referral Traffic from AI Platforms
Learn how to evaluate AI-platform referral traffic through sources, landing pages, engagement, and conversions without confusing visits with citations.
How to Combine Search Console and GA4 Data
Learn how Search Console and GA4 form one measurement journey, connecting impressions and clicks with engagement, conversions, and business outcomes.
How to Find Pages Losing Organic Traffic
Find pages losing organic traffic with Search Console and Analytics, then separate changes in demand, rankings, CTR, and measurement.
Impressions Are Up but Clicks Are Down: Why?
Rising impressions and falling clicks mean search exposure is growing while visits weaken. Learn what causes the pattern and how to interpret it.
064 articles
View category →Websites, SEO & AI
This cluster treats the website as an information asset and business environment that people, search engines, and AI systems need to understand. It focuses on principles, structure, and decision impact rather than coding tutorials or website-building services.
What Is a Website? How It Works and Why It Matters to Businesses
Learn what a website is, how it works, its business functions, and how clear information effectively connects people, search engines, and AI systems.
Business Websites as Sources of Information, Trust, and Conversion
Learn how a business website provides authoritative information, reduces uncertainty, and creates a controlled path towards meaningful conversion.
How Do Search Engines and AI Systems Read Websites?
Understand how search engines and AI systems access, process, and use website information without relying on exaggerated optimisation claims.
Modern Website Standards: Mobile, Speed, Security, and Accessibility
A modern website standard covers mobile experience, performance, security, accessibility, clear information, and sustainable maintenance.
074 articles
View category →Google Ads Analytics
This cluster explains how to investigate changes in cost and conversions before taking action. It separates tracking, campaign, landing-page, attribution, and platform causes.
Google Ads Analytics: Diagnosing Performance Changes
Learn how to analyse Google Ads performance and diagnose changes in cost, traffic, conversions, and business value before making optimisations.
How to Audit Google Ads Conversion Tracking
Audit Google Ads conversion tracking across goal definitions, tags, counting, attribution, consent, and reconciliation with CRM data.
Google Ads CPA Increased: What to Diagnose Before Changing Bids
Diagnose a higher Google Ads CPA across tracking, cost, conversion rate, traffic, landing pages, and lead quality before changing bids.
High Google Ads Clicks but Low Conversions: What Causes It?
Diagnose high Google Ads clicks but low conversions across tracking, search intent, traffic, landing pages, offers, and lead quality.
084 articles
View category →Marketing Decision Intelligence
Data does not automatically become a decision. This cluster separates findings, insights, and recommendations, then explains how to prioritise and test them while final control stays with people.
Marketing Decision Intelligence: A Practical Framework
Learn a practical marketing decision framework connecting evidence and insights with priorities, accountable action, experiments, and evaluation.
How to Build Hypotheses from Marketing Data
Learn how to turn a marketing finding into a specific, testable hypothesis that can support a real decision.
Designing Experiments to Test Marketing Recommendations
Learn how to connect a marketing hypothesis with controls, metrics, guardrails, and a business decision.
Root Cause Analysis for Digital Marketing Problems
Learn how to separate symptoms, triggers, and contributing causes before deciding how to address a digital marketing problem.