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Unlocking Creativity with Azure OpenAI

Unlocking Creativity with Azure OpenAI

By : AMIT MUKHERJEE, Adithya Saladi
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Unlocking Creativity with Azure OpenAI

Unlocking Creativity with Azure OpenAI

By: AMIT MUKHERJEE, Adithya Saladi

Overview of this book

Azure OpenAI, a cutting-edge service by Microsoft, harnesses the power of OpenAI's Large Language Model (LLM) to drive cloud-based innovations within enterprises. This service integrates advanced LLM models into business functions, transforming Microsoft products like GitHub Copilot, Microsoft 365 Copilot, and Bing Chat, making them more advanced and interactive. Azure OpenAI is accessible via REST APIs, Python SDK, or Azure OpenAI Studio, opening doors to build innovative AI applications. This book is a comprehensive guide to build GenAI applications using Azure OpenAI. It begins with the fundamentals, including how to access Azure OpenAI and how to effectively utilize its REST API and Python SDK. It takes a deep dive into various AI models and emphasizes the crucial aspects of prompt engineering and fine-tuning for optimal output. It further underlines the significance of content filters and prevention of misuse, maintaining a strong focus on safety and security protocols and finally the significance of Azure OpenAI Studio in deployment and administration is emphasized. Practical applications are showcased like content generations, summarization, semantic search, code documentation and code generation. Combining Azure Cognitive services amplifies Generative AI potential and finally aligns with Microsoft's ethical AI principles.
Table of Contents (19 chapters)
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Free Chapter
1
Part 1: Foundations of Generative AI and Azure OpenAI
5
Part 2: Practical Applications of Azure OpenAI: Real-World Use Cases
13
Part 3: Mastering Governance, Operations, and AI Optimization with Azure OpenAI

Prompt injection attacks in LLMs

Prompt injection attacks exploit vulnerabilities in LLMs by introducing malicious inputs designed to manipulate the model’s behavior. These inputs, often crafted with precision, can cause the model to generate unintended or unauthorized outputs, access restricted data, or execute harmful commands.

At their core, these attacks leverage the inherent trust placed in the inputs fed to an LLM. By embedding deceptive prompts, attackers can steer the model to produce inaccurate information or perform actions that compromise system integrity. The implications of such exploits are significant, particularly in systems where automated text generation plays a critical role.

While it’s challenging to eliminate the risk of prompt injection attacks, understanding how these tactics work the first step is in mitigating them. By adopting robust safeguards and regularly reviewing system interactions, it is possible to enhance the security and reliability...

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