Generative AI Design Patterns: A Comprehensive Guide

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Abstract (English)

As generative artificial intelligence (GenAI) continues to transform industries, there is a growing need for structured design patterns that address key challenges such as cost, latency, hallucinations, and domain-specific adaptation. This paper presents a comprehensive taxonomy of GenAI design patterns derived from an analysis of production-level implementations of Large Language Models (LLMs). The proposed patterns include Layered Caching for Adaptive Fine-Tuning, Multiplexing AI Agents as a Panel of Experts, Multi-Task Fine-Tuning for Robustness, Blended Rule-Based and Generative Approaches, Knowledge Graph Augmented LLMs, Swarm Intelligence for AI Agents, Modular Monolithic Architectures with Composability, Memory Cognition Strategies, and Red & Blue Team Dual-Model Evaluation. Each pattern is designed to enhance efficiency, reliability, and scalability while mitigating inherent GenAI limitations. By providing a structured approach to LLM deployment, these patterns serve as a foundational framework for AI engineers, researchers, and practitioners seeking to build high-performing generative AI systems. Future research should focus on empirical validation, pattern evolution, and integration with regulatory and ethical considerations in AI governance. Keywords: Generative AI, Large Language Models, AI Design Patterns, AI Architectures, Knowledge Graphs, Multi-Agent Systems, AI Evaluation Frameworks.