09 Generative AI¶
How the platform supports generative AI: managing documents, images and audio as data, making them searchable by meaning, building and evaluating applications on language models, choosing between retrieval and fine-tuning, and governing AI systems responsibly.
Before starting
02 Storage, 05 Governance and security and 08 Data science and ML, in particular Object storage and the S3 API, PII and data classification and The ML lifecycle and MLOps. Agents and MCP, in 06 Serving, covers agents that query governed data.
Pages, in reading order¶
- Unstructured and multimodal data: Data that is not rows and columns, such as documents, images, audio and video, stored in the lake alongside tables and made usable by extracting text, structure and metadata from it.
- Embeddings, vector search and RAG: Embeddings turn text, images or other content into lists of numbers that place similar meanings close together; vector search finds the closest ones quickly; retrieval-augmented generation (RAG) uses those results to give a language model the right documents to answer from.
- LLM applications and evaluation: Building applications on large language models, and measuring whether they work: choosing models, managing prompts, evaluating answers systematically, and tracing and monitoring them in production. The practices together are often called LLMOps.
- Fine-tuning or RAG (optional): The ways to adapt a language model to a task, prompting, retrieval, fine-tuning and distillation, what each changes, what data each needs, and how to choose between them.
- Responsible AI and AI governance: The practices and rules that make machine learning and AI systems accountable: knowing which systems exist, documenting how they were built, checking them for unfair or unsafe behaviour, keeping people in control, and meeting legal obligations such as the EU AI Act.