[Hack Update] 066-Updates #1036
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Updated section titles and improved clarity in the text.
Updated the scenario and tasks for Model Discovery to focus on chatbot use cases instead of biotech news articles. Adjusted hints and metrics for model evaluation.
Added examples of model choices for students to consider during model discovery and benchmarking.
Updated scenario description for model comparison.
Added possible model choices for comparison in the document.
Added information about the gpt-5-nano-2025-08-07 model and its use cases.
Added a note to complete Challenge-00 before proceeding.
Added instructions for deploying alternative models and modifying the bicep file.
Added instructions for deploying custom models using the Bicep file.
Added notes on model deployment and usage in Jupyter notebooks.
Updated model deployment instructions and clarified usage of the bicep file for deploying models. Enhanced formatting for better readability.
Revised text for clarity and grammatical accuracy.
jrzyshr
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Oct 5, 2025
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Thx for the updates. One minor grammar issue in C4 to fix.
C1 Jupyter notebook needs more updates. Will discuss offline.
| "An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar.\n", | ||
| "\n", | ||
| "Different Azure OpenAI embedding models are specifically created to be good at a particular task. Similarity embeddings are good at capturing semantic similarity between two or more pieces of text. Text search embeddings help measure long documents are relevant to a short query. Code search embeddings are useful for embedding code snippets and embedding nature language search queries.\n", | ||
| "Different Azure OpenAI embedding models are specifically created to be good at a particular task. Similarity embeddings are good at capturing semantic similarity between two or more pieces of text. Text search embeddings help measure long documents are relevant to a short query. Code search embeddings are useful for embedding code snippets and embedding natural language search queries.\n", |
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"Text search embeddings help measure..." This doesn't sound grammatically correct?
Corrected the spelling of 'Codespace' in the deployment instructions.
…hatTheHack into 066-updateUST merge previous updates
jrzyshr
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Oct 10, 2025
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Thanks for the updates!
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This pull request updates the student and coach materials for Challenge-02 and Challenge-03 in the OpenAI Fundamentals course, focusing on improving clarity, relevance, and alignment with current Azure AI Foundry features. The changes introduce more realistic scenarios, update terminology to match platform features, and encourage model selection based on use case rather than fixed model comparisons.
Challenge-02 Content Updates:
Model Selection and Comparison:
gpt-3.5togpt-4o. Example model choices and their strengths are provided to guide discussion and selection. [1] [2]Scenario and Terminology Alignment:
Comparison Task Improvements:
Challenge-03 and Notebook Updates:
Prerequisites Clarification:
Model Usage Guidance: