Cohesion metrics measure how well the methods of a class are related to each other. A cohesive class performs one function while a non-cohesive class performs two or more unrelated functions. A non-cohesive class may need to be restructured into two or more smaller classes.

High cohesion is desirable since it promotes encapsulation. As a drawback, a highly cohesive class has high coupling between the methods of the class, which in turn indicates high testing effort for that class.

What is MySQL?

MySQL is a relational database management system, used by customers across the world from small home-grown applications up to some of the largest data centers. It is developed, distributed and supported by Oracle, who acquired the product from Sun Microsystems in 2010 when Oracle bought Sun.

In the current software development era, multiple teams work on numerous projects. We need to store the codebase in some version control system like GitHub or TFS. Many organizations add different projects to a single repository, which is known as a monorepo. Many big organizations like Google, Microsoft, and Uber use a monorepo in their application development.

In this blog post, you will learn how to:

Introduction: What is LLM?

Large Language Models (LLMs) serve as foundational elements in the development of artificial intelligence applications. They process and generate text that mimics human communication, thus making digital interactions feel more natural. Nowadays, these models play an essentail role across numerous sectors. They improve operational processes and enrich the customer journey. By analyzing vast amounts of data, LLMs offer insights previously beyond reach.

LLM engineering involves much more than just prompt design or prompt engineering. In this article, we share a set of engineering practices that helped us deliver a prototype LLM application rapidly and reliably in a recent project. We'll share techniques for automated testing and adversarial testing of LLM applications, refactoring, as well as considerations for architecting LLM applications and responsible AI.

Step into the realm of language magic with our in-depth tutorial on Large Language Model (LLM) use cases and applications. From personalized recommendations to smart chatbots, discover how these linguistic powerhouses are revolutionizing industries.

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Now, I will execute this query to get the total sales per country.

[('USA', 523.0600000000003), ('Canada', 303.9599999999999), ('France', 195.09999999999994), ('Brazil', 190.09999999999997), ('Germany', 156.48), ('United Kingdom', 112.85999999999999), ('Czech Republic', 90.24000000000001), ('Portugal', 77.23999999999998), ('India', 75.25999999999999), ('Chile', 46.62)]The total sales per country are as follows:

1. USA: $523.06
2. Canada: $303.96
3. France: $195.10
4. Brazil: $190.10
5. Germany: $156.48
6. United Kingdom: $112.86
7. Czech Republic: $90.24
8.

This guide demonstrates how to build a Retrieval-Augmented Generation (RAG) system using LlamaIndex and Milvus.

The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using a vector similarity search engine like Milvus, and then uses a generative model to generate new text based on the retrieved documents.