Generative AI Tools and Frameworks

Overview

This page catalogs the essential tools and frameworks for developing, evaluating, and deploying generative AI applications. These resources provide the foundation for building robust LLM-based systems and ensuring their quality and reliability.

Evaluation Frameworks

RAGAS Framework

RAGAS is an evaluation framework specifically designed to assess the performance of Retrieval-Augmented Generation (RAG) systems. It provides comprehensive metrics including faithfulness, answer relevancy, context relevancy, and context precision, offering insights beyond traditional language model evaluation.

Key Features: - LLM-based and non-LLM-based evaluation options - Specialised metrics for RAG system assessment - Automated evaluation capabilities - Standardised metrics across different implementations

LLM Evaluation

LLM evaluation methodologies are crucial for understanding model performance and effectiveness across various applications. This includes assessing text generation quality, context comprehension, and task-specific performance.

Development Frameworks

Llama Stack

Llama Stack is a comprehensive framework designed to standardise and streamline the development of generative AI applications. It provides unified APIs for various functionalities including inference, RAG, agents, tools, safety, and evaluations.

Key Features: - Unified API layer for consistent development experience - Plugin architecture supporting diverse implementations - Prepackaged distributions for quick deployment - Multiple developer interfaces (Python, Node, Swift, Kotlin) - Support for Llama model family including Llama 3.3

Integration Patterns

RAG Implementation

Retrieval-Augmented Generation (RAG) represents a key architectural pattern for enhancing LLMs with external knowledge. Understanding RAG principles is essential for building effective generative AI applications.

Components: - Document store and vector databases - Retrieval systems and similarity search - LLM integration and response generation - Evaluation and quality assessment

Best Practices

When working with generative AI tools and frameworks:

  1. Evaluation First: Always implement evaluation metrics before deploying LLM applications
  2. RAG Considerations: Use RAG patterns when external knowledge is required
  3. Framework Selection: Choose frameworks that align with your deployment requirements
  4. Continuous Assessment: Regularly evaluate system performance and update metrics
  5. Safety and Reliability: Implement appropriate safety measures and monitoring

Getting Started

For those new to generative AI development:

  1. Start with LLM evaluation concepts to understand assessment principles
  2. Explore RAG patterns for knowledge-enhanced applications
  3. Use RAGAS for comprehensive RAG system evaluation
  4. Consider Llama Stack for unified development workflows

This toolkit provides the essential resources for building, evaluating, and deploying generative AI applications effectively.