LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Batching and Asynchronous Operations

Implement batch processing for embedding generation and asynchronous calls to LLM APIs to enhance throughput.

Boosting RAG Performance

Your RAG application is working, but is it fast and cost-effective? As user traffic grows, you'll need to optimize how your app interacts with LLMs and vector databases.

In this lesson, we'll explore two powerful techniques: batching and asynchronous operations. These can significantly improve your RAG system's throughput and reduce operational costs.

One Task at a Time

Imagine you have a list of documents, and you need to get embeddings for each one. In a synchronous approach, your program would process each document one by one.

It sends a request for Document 1.
It waits for the embedding to return.
Then, it sends a request for Document 2.
It waits again.

This "wait-and-process" model is simple but can be very slow, especially with many I/O operations like API calls.

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