Generative AI

In this module, you will learn:

• What Generative AIiGo to glossary for generative AI (opens in a new tab)Models that produce new content rather than classifying or scoring content that already exists. is and how it works, including:
  • What Large Language Models (LLMsiGo to glossary for large language model (opens in a new tab)A model trained on text to predict the next token, and so to generate language.) are and how they differ from other AI models.
  • The limitations of Generative AI models, including hallucinationiGo to glossary for hallucination (opens in a new tab)A false statement made confidently by a language model. and access to data.
  • How providing context can improve the responses from Generative AI models.
• How you can use Retrieval Augmented Generation (RAGiFull definition for retrieval-augmented generation (opens in a new tab)Fetching relevant context from an external data source as additional context to inform a language model's response.) to improve GenerativeAI model responses.
• The benefits of GraphRAGiFull definition for GraphRAG (opens in a new tab)Retrieval-augmented generation whose context comes from a knowledge graph, so the model can follow the relationships between facts. techniques for enhancing information retrieval.
• How knowledge graphs structure data to represent real-world entities and their relationshipsiFull definition for relationship (opens in a new tab)A named, directed connection between two nodes. Every relationship has a type, a start node and an end node..

If you are ready, let's get going!