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June 30, 2025, 10:54 a.m.
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SOURCE: Singh R. Mastering Generative AI.Design Patterns Frameworks Real-World Apps 2025
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COVER
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MEDIAINFO
Textbook in PDF format This book offers a practical, end-to-end guide to Generative AI System Design, blending core concepts with real-world applications. Developing generative AI applications at the enterprise level demands significantly higher standards. Organizations require frameworks that not only support large-scale AI deployments but also ensure robust security and seamless integration with existing IT ecosystems. These frameworks must be capable of managing the intricacies of enterprise environments while remaining adaptable to shifting business priorities. Frameworks like TensorFlow and PyTorch have emerged as leading choices in this space, offering comprehensive libraries, strong community backing, and versatile integration options. Their scalability and flexibility make them well-suited for enterprise use, enabling the development of custom AI models tailored to specific business objectives. This empowers organizations to harness AI effectively and maintain a competitive advantage in their industry. Selecting the right model for your use case in Generative AI is a critical architectural and business decision. It affects performance, cost, compliance, accuracy, speed, and user experience. Here's a detailed guide explaining the strategies and frameworks you can use to decide which Large Language Model (LLM) or generative model to adopt, along with reference material and structured criteria. Different model families have distinct strengths. For general reasoning tasks, models like GPT-4 and Claude 3 excel. If speed and cost efficiency are priorities, consider Mistral or GPT-3.5. For code generation, specialized models like Codex or Code LLaMA might be better choices. When working with visual inputs, models with vision capabilities like Gemini or GPT-4V are necessary. For tasks requiring extensive context, Claude 3 Opus or Gemini 1.5 with their expanded context windows may be preferable. Code examples are in Python. Key topics include GenAI & ML Foundations: Neural networks, transformers, attention mechanisms, and tokenization. Model Efficiency: Techniques like parallelism, quantization, pruning, and knowledge distillation. Core Components: Embeddings, scene graph generation, text-to-phoneme conversion, and more. Design Framework: A structured 7-step approach for building scalable GenAI systems. System Blueprints: Detailed system design for Text-to-Text, Text-to-Speech, Text-to-Image, and Text-to-Video generation. Tech Stack: Overview of GenAI frameworks, infrastructure, and tools. Interview Prep: Tips, resources, and AI-driven evaluation for system design interviews. Future Trends: Responsible AI, GenAI for low-power devices, and next-gen models. Case Studies: Real-world GenAI application Preface Introduction to Generative AI System Design Fundamental Concepts in Generative AI GenAI Evaluation Methods Inference System Design for GenAI Systems Key Concepts in Designing GenAI Systems Generative AI tech stack: Frameworks, infrastructure, models and applications Generative AI Design Patterns System Design Use Cases Text-to-video generation system Tex-to-image generation system Text-to-text generation system Summary Future Trends
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