Essential Math for AI-Native Development

Build better AI systems by understanding the math that drives them

Developers no longer need to train models from scratch to build powerful AI products. But using an API is not the same as understanding why an AI system retrieves the wrong information, ranks weak results highly, produces confident but incorrect answers, or performs well against one metric and poorly in the real world.

Behind embeddings, semantic search, RAG, recommendations and LLM outputs are a small number of mathematical ideas that increasingly influence everyday engineering decisions.

This workshop makes those ideas practical.

Rather than teaching abstract theory or advanced equations, Essential Math for AI-Native Development focuses on the mathematical intuition developers need to build, evaluate and improve modern AI features. Through practical explanations and hands-on exercises, you’ll explore how vectors represent meaning, how similarity affects retrieval, why search is fundamentally a ranking problem, how probability shapes model outputs and what evaluation metrics reveal—or fail to reveal—about system quality.

If you already build with AI tools and APIs but want to understand what is happening beneath the surface, this workshop will help you make stronger technical and product decisions.

What you’ll learn

By the end of the workshop, you’ll be able to:

• Understand vectors and embeddings in practical, product-focused terms
• Reason about similarity, distance, ranking and retrieval quality
• Recognize how different similarity methods can change search results
• Understand how probability influences model outputs
• Distinguish confidence from correctness in AI systems
• Apply precision, recall, relevance and other evaluation concepts more effectively
• Understand how optimization objectives shape model behaviour
• Diagnose common problems in search, RAG, recommendations and assistant workflows
• Make better engineering decisions without needing an advanced mathematics background

What you’ll work through

The workshop connects each concept to real AI development scenarios, including:

• Representing meaning with vectors and embeddings
• Comparing similarity and distance
• Ranking results for semantic search and retrieval
• Evaluating whether a RAG system is retrieving useful context
• Interpreting probability, confidence and uncertainty
• Choosing metrics that reflect actual product quality
• Understanding why models produce unexpected results
• Reasoning about how an AI system could be improved

What you’ll leave with

You’ll receive:

• A practical mental model for the mathematics behind modern AI
• Hands-on exercises connected to real development workflows
• Reusable frameworks for evaluating retrieval and output quality
• Practical implementation patterns
• Workflow templates and reusable systems
• Full workshop recording
• Certificate of completion

More importantly, you’ll leave with the vocabulary and intuition to discuss AI systems more precisely, investigate failures more effectively and make better decisions when building AI-powered products.

Who should attend?

This workshop is ideal for:

• Full-stack developers building AI-enabled applications
• Engineers working with semantic search, RAG, recommendations or LLM features
• Developers using AI APIs who want to understand the systems beneath them
• Technical leads making AI architecture and product decisions
• Product-minded builders seeking practical AI intuition without an academic ML course

No advanced mathematics or machine-learning background is required. The workshop is designed to be approachable, visual and directly relevant to modern software development.