Practical guides to AI, Python, Git, SQL, Web/API, SAP/ERP and mathematical models, with explanations, code examples, sources and limitations.
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A technical guide explaining why syntactically correct JSON from Large Language Models (LLMs) is insufficient for production systems and how JSON Schema provides the necessary structural and type guarantees.
Learn how to effectively evaluate the precision and recall of AI models, especially when working with small question datasets. This guide covers the concepts, calculation methods, and practical considerations using scikit-learn.
Use If-None-Match to revalidate cached responses and If-Match to avoid overwriting a newer representation.
Read training and validation scores together, fit preprocessing inside a pipeline and keep a final test set out of model selection.
Explore the principles of semantic search, its reliance on embeddings, and the reasons why text that appears similar might not provide the desired answer. Learn about vector search, hybrid search, and their applications in information retrieval.
Understand the differences between Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), how to choose the right metric for your prediction task, and the impact of large errors on each.
A guide to using the Python csv module to automatically detect delimiters, handle Unicode Byte Order Marks (BOM) with utf-8-sig, and manage platform-specific newline issues using the Sniffer class and correct file opening parameters.
Build a previous-season reference, evaluate it on later observations and distinguish calendar alignment from leakage and missing data.
Use chronological splits and fit preprocessing only on past data.