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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Step-by-step guide to creating an index, generating embeddings, and executing hybrid queries that combine full-text and vector search using Azure AI Search.
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.
Find the expensive step and distinguish estimates from measurements.
Diagnose repeated JOIN rows by counting matching keys, checking constraints and choosing whether the result should contain orders, items or summaries.
Distinguish response access from request sending, inspect an OPTIONS preflight and configure explicit origins for credentialed browser requests.
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.
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.
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.
Preserve shared history or replay local work before integration.
Separate authentication, permissions, rate limits and service availability before retrying.
Use If-None-Match to revalidate cached responses and If-Match to avoid overwriting a newer representation.
Understand the differences between offset-based and cursor-based pagination and when to use each, especially when dealing with frequently changing datasets.
Read training and validation scores together, fit preprocessing inside a pipeline and keep a final test set out of model selection.
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.
Learn how to parse JSON data in Python from both string literals and files, and how to effectively handle potential decoding errors using the built-in json module.
How Python resolves relative paths depends on the process working directory, which varies by launcher. This guide shows how to inspect the working directory, choose and document an explicit path base, and understand why __file__ cannot always be trusted.
A small, repeatable setup for a project and a quick check that pip uses the right Python.
How retrieval connects a language model to documents, and why a citation is not a guarantee.
An explanation of how SAP S/4HANA links Financial Accounting (FI) and Management Accounting (CO) through organizational objects.
In SAP S/4HANA, a payment to a vendor settles an existing liability from a posted invoice; it is not a new expense. Tracing the payment back to the original invoice is done using clearing documents, assignment fields, and line item displays.
Understand financial reporting, internal cost allocation and their connection in S/4HANA.
A practical order for comparing SAP reports: organizational scope, ledger, fiscal period, currency and line-item detail.
Understand IS NULL, three-valued logic and the difference between COUNT(*) and COUNT(column).
Transactions in PostgreSQL allow grouping SQL operations into atomic blocks using the commands BEGIN, COMMIT, and ROLLBACK. This ensures data integrity: either all changes are applied or none at all. The mechanism is based on ACID principles - atomicity, consistency, isolation, and durability.