Generative AI and Large Language Models

Overview

Generative AI and Large Language Models (LLMs) represent a transformative shift in artificial intelligence, enabling machines to generate human-like text, understand context, and perform complex reasoning tasks. This section explores the key concepts, frameworks, and evaluation methodologies essential for working with these technologies.

Core Concepts

Large Language Models (LLMs)

LLMs are neural networks trained on vast amounts of text data, capable of understanding and generating human language. They form the foundation of modern generative AI applications, from chatbots to content creation tools.

Retrieval-Augmented Generation (RAG)

RAG enhances LLMs by combining them with retrieval systems that fetch relevant information from knowledge bases. This approach allows models to access external, up-to-date information during generation, improving accuracy and reducing hallucinations.

Evaluation and Assessment

LLM Evaluation

LLM evaluation is crucial for understanding model performance and effectiveness. It involves assessing how well models generate human-like text, comprehend context, and perform specific tasks across various applications.

Tools and Frameworks

For comprehensive information about evaluation frameworks, development tools, and best practices, see the dedicated Tools and Frameworks page. This includes detailed coverage of:

  • RAGAS Framework: Specialised metrics for evaluating RAG systems
  • Llama Stack: Comprehensive development framework for generative AI applications
  • Integration patterns and best practices
  • Getting started guidance for new developers

Key Benefits

  • Enhanced Accuracy: RAG systems provide more accurate and verifiable information
  • Fresh Knowledge: Knowledge bases can be updated independently of model training
  • Transparency: Retrieved documents provide clear sources for generated content
  • Efficiency: Reduces the need for frequent model retraining

Applications

Generative AI and LLMs find applications across numerous domains: - Content generation and summarisation - Question answering systems - Code generation and assistance - Creative writing and storytelling - Educational tools and tutoring systems - Business process automation