Gen-AI技术

The advent of large language models (LLMs) , trained on vast amounts of text data, has been one of the most significant breakthroughs in natural language processing. The ability of these models to generate remarkably fluent and coherent text with just a short prompt has opened up new possibilities for conversational AI, creative writing, and a wide array of other applications.

Prompt engineering, or prompt design, is crafting instructions for LLMs to get desired responses. It’s essential for ensuring accurate, high-quality responses from a large language model.

Implementing RAG presents a challenge, especially when it comes to effectively parsing and understanding tables in unstructured documents. This is particularly difficult with scanned documents or documents in image format. There are at least three aspects of these challenges:

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.