Definitions and explanations of key AI terms
Capabilities of AI tools mentioned in the Chapter 1 recap. Day 3 focuses on five core capabilities of Generative AI, including summarisation and content creation.
You can think of the foundation of AI usage outlined in Chapter 1 like driving a car: The Precision Stack is setting your GPS and defining the destination and route (the instructions). The Three-Gate Check is checking your blind spots and verifying the route (Trust but Verify). RAG/Chunking is organizing the contents of your glove box (the data) so you can find the map you need instantly without reading every piece of paper (saving time and money—tokens).
The risk that AI perpetuates or amplifies societal biases present in its historical training data, leading to discriminatory outcomes (e.g., in recruitment or loan approval).
The solution to the context window constraint, where large documents are broken into smaller, linkable sections to improve cost efficiency and focus answers.
The limit on the amount of text (context) an AI model can consider at once, functioning as its short-term memory. If a document exceeds this limit, it must be broken into sections (chunking).
One of AI's six core capabilities (introduced in Chapter 1, Day 3) that allows it to interpret questions and generate detailed responses. Deep Research is described as an extension of this capability.
An extension of Conversational Intelligence that actively searches the web in real-time, browses multiple sources, cross-references, and synthesizes findings into structured reports.
A map of meaning (smart tags) that helps AI find the most relevant chunks of data instead of reading everything, thereby saving tokens and cost.
Different business questions require different answer types, including: Yes/No (Classification), A number (Regression), A priority list (Ranking), Natural groupings (Clustering), and A forecast over time (Time-series forecasting).
AI focused on content creation (text, images, code, etc.) using patterns learned from vast data, driven by instructions provided as text (prompts). Its accessibility stems from using Natural Language Processing (NLP).
Garbage In, Garbage Out. States that poor data quality leads directly to poor AI performance, producing confidently wrong outputs that look correct but are flawed.
When AI confidently states plausible-sounding information that is factually incorrect or entirely invented. This is classified as a reputational risk requiring human verification.
An operating model that balances central control (Hub) for governance and standards (Guardrails, LLMOps) with decentralized speed (Spokes) for product delivery and specific use case execution.
LLMOps focuses on the engineering of the model (fine-tuning, version control). AgentOps is the new safety discipline focused on the agent's actions (identity, permissions, audit trails, kill-switches).
The gradual degradation of AI performance because the real-world data it encounters (concept drift) or the distribution of inputs (data drift) has changed from its original training data.
The four minimal stages an AI model must follow to remain dependable: Train, Validate, Deploy, Monitor & Improve.
The AI-first mindset where you define what the product learns to achieve (e.g., increase conversion) rather than defining static features.
A structured framework comprising Persona, Task, Context, and Output Specification used to make prompts reliable and precise. This framework ensures the AI understands the audience, constraints, and required format, reducing ambiguity.
The system that combines chunking and embeddings to retrieve the most relevant evidence from a knowledge base before the model generates an answer. This grounds the AI in facts and is used to mitigate hallucination risk.
A systematic scoring framework used to prioritize features based on four components: Reach, Impact, Confidence, and Effort.
A framework used to prioritise AI risks by consequence and timing, categorizing them into: Stop-Work Risks (Priority 1, legal/regulatory threats), Reputational Risks (Priority 2, trust/brand damage), and Operational Risks (Priority 3, quality/cost issues).
Running the same prompt multiple times to compare outputs. Wildly different outputs signal that the prompt lacks sufficient constraints (a prompt quality issue).
A progressively rigorous methodology used to verify AI outputs, essential for managing the risk of hallucinations. It consists of: Gate 1: Immediate Scan (checking format/tone); Gate 2: Validation (fact-checking critical claims, testing stability); and Gate 3: Integration (embedding human sign-off and ongoing monitoring).
The small chunks of text (roughly 4 characters each) that AI systems process, representing the unit of cost and the measurement limit for the context window.
AI focused on prediction (e.g., house price, fraud score) based on past trends, often referred to as Machine Learning, and typically focused on numeric datasets.