Course Intro

What this lesson covers

This lesson introduces the AI Engineering course and explains its scope. The course concentrates on two building blocks of modern AI systems: large language models and vector databases. The video points out that many organisations are reworking their chat systems, search engines and recommendation engines with AI, and that these two components are common to all of those efforts. For software engineers, the course aims to explain how such systems are built internally and how to use them in their own projects. For product managers and technical leads, it aims to show what AI can do and how it might be brought into an organisation. According to the notes, the course includes detailed breakdowns of vector databases, indexing, large language models and the attention mechanism. There are no formal prerequisites, though some familiarity with neural networks and software engineering helps. The lesson also describes how the course works: questions posted in the comments receive a first answer from an AI bot and can be tagged for a human response, each lesson has a references tab for further reading, and coding assignments cover the practical side. The course is expected to take around two weeks and ends with a shareable certificate.

• The course focuses on large language models and vector databases, which are common to AI chat, search and recommendation systems
• It is aimed at software engineers moving into AI work, and at product managers and tech leads evaluating AI
• Topics include vector databases, indexing, LLMs and the attention mechanism
• There are no prerequisites, though basic neural network and software engineering knowledge helps
• Coding assignments are included, and the course takes about two weeks to complete