This chapter covers
- Understanding retrieval-augmented generation (RAG)
- Implementing Airflow tasks to populate a vector database with your content
- Retrieving relevant documents from a vector database using vector similarity search
- Using a large language model to generate content based on your own knowledge base
In recent years, the generative AI (GenAI) revolution has reshaped the way we create text, audio, and image-related content. GenAI systems have emerged as powerful tools capable of generating coherent, contextually relevant text that closely mimics human writing, opening new possibilities across various sectors, from marketing and copywriting to education and customer service.
Having high-quality data is paramount to building a good GenAI system or product because poor input data inevitably leads to poor results. Fortunately, Airflow can play an important role in ensuring high-quality input data by automating the processes involved in data preparation. In this chapter, we’ll explore Airflow’s role in building robust GenAI solutions with an example use case involving family recipes.
As we navigate this new era, the demand for high-quality, curated data has never been greater. Organizations and individuals alike are recognizing the importance of preparing, organizing, and providing access to their data pipelines to fuel GenAI applications.