Building generative AI applications that use retrieval augmented generation (RAG) can pose a host of challenges. Let’s look at troubleshooting RAG implementations that rely on vector databases to retrieve relevant context that’s then included in a prompt to a large language model to provide more relevant results.

We will break this process down into two main parts. The first, which we’ll address in this first article in the series, is the embedding pipeline, which populates the vector database with embeddings:

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