If you gave up on multithreading in Python I have good news. It’s very likely it will change and you’ll be able to disable GIL very soon, but there’s a catch

Python, what a name for a programming language. It could be named anything ranging from Lizard to Labrador, but it was called after a snake*.

Generative AI (GenAI) enables advanced AI use cases and innovation but also changes how the enterprise architecture looks like. Large Language Models (LLM), Vector Databases, and Retrieval Augmentation Generation (RAG) require new data integration patterns and data engineering best practices.

If you’re building any meaningful product/feature with LLMs (large language models), you’ll probably use the technique called RAG (retrieval-augmented generation). It can allow you to integrate external data that was not available in the LLM’s training data into the LLM’s text generation process, which can greatly reduce the nightmare of hallucination and improve the relevance of the text responses.

This article is part of a larger series on using large language models in practice. In the previous post, we fine-tuned Mistral-7b-Instruct to respond to YouTube comments using QLoRA.

A RAG system is an innovative approach to information retrieval. It utilizes traditional information retrieval approaches like vector similarity search combined with state-of-the-art large language model technology. Combined, these technologies make up a robust system that can access vast amounts of information from a simple prompt.