Posts tagged with: LLM Development
Content related to LLM Development
12-Factor Agents: Guide to Reliable LLM Applications
Discover the 12-Factor Agents, an open-source framework and set of principles for building robust, scalable, and maintainable AI applications powered by Large Language Models (LLMs). Authored by Dex Hadfield, this project addresses the common challenges in deploying LLM-based software to production, offering practical guidance for developers. Learn how to transition from experimental AI agent development to creating reliable, production-grade LLM applications. The guide covers crucial aspects like prompt management, context window optimization, unified state management, and error handling, providing insights essential for any developer looking to master LLM-powered software engineering. Explore the principles that empower you to deliver high-quality, customer-facing AI agents.
Agents Towards Production: Build & Deploy GenAI Agents
Learn to build and deploy production-grade Generative AI agents with 'Agents Towards Production,' an open-source GitHub repository offering end-to-end, code-first tutorials. This incredible resource guides you from initial concept to scalable real-world deployment, covering essential aspects like orchestration, memory, observability, and security. Dive into practical examples for integrating tools, fine-tuning models, and ensuring robust performance. Perfect for developers looking to master AI agent development and bring their AI projects to fruition.
RAGbits: Rapid Development for GenAI Applications
Discover RAGbits, an open-source framework designed to accelerate the development of reliable and scalable Generative AI applications. This innovative toolkit provides modular components for building sophisticated RAG (Retrieval-Augmented Generation) pipelines, managing LLMs, and integrating various data sources. Learn how RAGbits simplifies complex tasks like data ingestion, vector store management, and chatbot deployment, enabling developers to create robust AI solutions efficiently. Explore its features, including type-safe LLM calls, extensive format support, and built-in testing tools, to streamline your GenAI projects.