From the course: Azure for Developers: Retrieval-Augmented Generation (RAG) with Azure AI
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Summary and next steps - Azure AI Services Tutorial
From the course: Azure for Developers: Retrieval-Augmented Generation (RAG) with Azure AI
Summary and next steps
- [Instructor] Congratulations. We have completed the course. Let us summarize what we have covered so far. We discussed that RAG is the technique of adding data to a language model from an external data source. This allows businesses to chat with their own data. We discussed the concepts of tokens and embeddings and how they are important to determining your cost and finding similarities with your data. We discussed the concept of a vector database and how it is used to store your embeddings. We also discussed the Azure OpenAI embedding model and how it was used throughout the course to convert our text into embeddings. We then showed you how we can create a RAG solution via Azure AI Foundry, but there's a more detailed LinkedIn Learning course on the said topic. We then focus on creating a RAG solution using Azure AI Search. It involved the following steps. First, we created an index that contains your fields, vector search configuration, semantic configuration, and many more. We…
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