This book dives into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges, and focuses on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.
Abstract
Explore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.
With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.
We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.
Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.
As work continues to evolve, Artificial Intelligence (AI), especially generative AI and Large Language Models (LLMs), is proving essential in augmenting our workflows. This session will explore integrating AI into traditional process improvement practices, demonstrating how it can enhance effectiveness and provide part...
This book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques, and takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.
An overview of AI’s role across the stages of scientific research is provided, illustrating how AI can assist researchers by aggregating and synthesizing a large body of work across various domains, supporting methodological implementation, and facilitating communication and publication.
Neda Sadeghi, Erin Nakamura, Luke J. Norman et al.· Aperture Neuro· 0 citations
The concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure.
Gener...
The proposed FlowGen uses LLM-based Semantic Information Processing (SIP) to extract semantic elements, constructs a Semantic Relational Graph (SRG) encoded by an enhanced R-GAT for basic flow generation (BFGen), and further supports branch point prediction through BPP and branch-conditioned alternative flow generation...
Guang-Yu Wang, Bangqi Li, Ji Wu et al.· 0 citations
Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered t...
Paul Iusztin, Maxime Labonne· 2 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.