An unforgettable meetup experience with the perfect view of the Atlantic Ocean, speaking right next to where you board the ferry to the historically significant Robben Island.
Session: Building AI Solutions in Azure AI Foundry
Following the philosophy “if it’s not broken, it doesn’t need fixing,” I redelivered the same session from Johannesburg. However, this experience was enhanced by:
- More questions and interaction
- Even more engaged audience
- Opportunity to attend colleagues’ sessions
- Better learning experience overall
The session serves as a perfect introduction to Azure AI Foundry and comprehensive AI solution building.
Deep Dive: Retrieval-Augmented Generation (RAG)
Special focus on RAG, a technique that enhances LLM responses by incorporating your own data.
How RAG Works:
- Data Sourcing and Formatting: Collect and store your data in one location.
- Depending on its size, you may need to break it into smaller chunks a process called chunking.
- To make the data understandable for the model, it must be converted into a numerical format, known as vectorization.
- Once vectorized, a search index makes the data easily searchable just like a library catalog.
- Finally, to ensure relevant results, the retrieved data is ranked based on importance.
- Retrieval: When a user asks a question, the app searches the database for relevant information.
- Augmentation: The retrieved data is added to the prompt to enhance the response.
- Generation: The enhanced prompt is fed to the LLM, which generates a more accurate and context-aware response.
Sounds simpler now, right? You can learn more about implementing RAG in our Generative AI for Beginners course.
Resources
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