AI Skills for All

What AI really is (and what it isn't)

Clear definitions of AI, machine learning, deep learning and generative AI, how they fit together, and what today's AI can and cannot do.

How machines learn: data, training and models

What training data is, how a model improves by correcting its mistakes, the three main types of learning, and why good data matters more than clever algorithms.

How large language models work

Tokens, next-token prediction, training and fine-tuning, the context window, temperature and knowledge cut-offs: the few ideas that explain almost everything ChatGPT, Claude and Gemini do.

Why AI gets things wrong: hallucinations, bias and limits

Why fluent answers can be false, where bias comes from, the common failure patterns of language models, and a simple routine for checking AI output before you rely on it.

Prompting fundamentals: getting useful answers

The six parts of a strong prompt, before-and-after examples for real work tasks, how to iterate, and reusable templates for developers, testers, analysts and managers.

Using AI safely at work

What you can and cannot paste into AI tools, approved versus personal tools, prompt injection, intellectual property, being open about AI use, and a one-page checklist for your team.

Advanced prompting and structured output

Break big jobs into a chain of small prompts, get JSON and tables other tools can use, write reusable prompt templates, and make prompts that work across hundreds of inputs, not just one.

Embeddings and semantic search

How AI turns text into lists of numbers that capture meaning, why that lets search find "reset my password" when the document says "forgotten login", and where embeddings are used in real products.

Retrieval-augmented generation (RAG)

How to make AI answer from your own documents, with sources: the two halves of a RAG system, what makes it good or bad, how it compares with fine-tuning, and the questions to ask before building one.

Tool calling and AI agents

How a model asks software to take actions, how an agent loops through planning, acting and checking, when an agent is worth it versus a simple workflow, and how to keep agents safe.

Evaluating AI quality

Why pass/fail tests are not enough for AI, how to build an evaluation set, which measures to use (from exact match to rubrics, human review and AI judges), and how evaluation becomes a habit, not a one-off.

Cost, speed and scale

How AI features are priced, a worked cost estimate, why some answers feel slow, and the levers teams use to make AI cheaper and faster: shorter context, the right model, caching, batching and limits.

AI across the software lifecycle

Where AI helps in discovery, requirements, design, coding, testing, release, operations and support, what to keep human, how teams adopt it without chaos, and how to measure whether it is really helping.