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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

AOAI model context window

In the world of Large Language Models (LLMs), a context window defines the amount of text a model can process at once, impacting how it generates and comprehends language. This window is measured by the number of tokens (either whole words or fragments), directly affecting how much information the model uses to predict the next token. In simple terms, it dictates how much context the model takes into account when forming predictions or crafting responses.

For example, the GPT-3.5-Turbo (0125) model context has 16,385 input tokens and 4,096 output tokens, while the GPT-4o and GPT-4o mini models have a much larger number at 128,000 input tokens and 16,384 output tokens. For information about the context window of AOAI models, visit https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?#gpt-4-and-gpt-4-turbo-models.

The trend in LLMs is moving toward bigger context windows, which enable more detailed and coherent outputs. However, this...

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