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
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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.

Large Language Models (LLMs) are a type of artificial intelligence model that can generate human-like text. They are trained on large amounts of text data and can be used for a variety of natural language processing tasks, such as language translation, question answering, and text generation.

Evaluating LLMs is important to ensure that they are performing well and generating high-quality text. This is especially important for applications where the generated text is used to make decisions or provide information to users.

Over the past year, excitement around Large Language Models (LLMs) skyrocketed. With ChatGPT and BingChat, we saw LLMs approach human-level performance in everything from performance on standardized exams to generative art. However, many of these LLM-based features are new and have a lot of unknowns, hence require careful release to preserve privacy and social responsibility. While offline evaluation is suitable for early development of features, it cannot assess how model changes benefit or degrade the user experience in production.

The question answering system, that is based on semantic search and LLM currently one of the most popular application of LLM functionality. But what after we build it? How to evaluate the work of QnA system?

Although evaluating the outputs of Large Language Models (LLMs) is essential for anyone looking to ship robust LLM applications, LLM evaluation remains a challenging task for many. Whether you are refining a model’s accuracy through fine-tuning or enhancing a Retrieval-Augmented Generation (RAG) system’s contextual relevancy, understanding how to develop and decide on the appropriate set of LLM evaluation metrics for your use case is imperative to building a bulletproof LLM evaluation pipeline.

• Prerequisites
• Answering questions is complicated