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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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.
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.
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.
A practical guide to selecting a decision threshold by weighting false‑positive and false‑negative costs, using a held‑out validation set and scikit‑learn confusion matrices.
Distinguish storage, validation and shared caching. Three response policies show when to use public max-age, private no-cache and no-store.
Separate a request timeout from the total retry budget, interpret Retry-After, and avoid duplicating writes when the server outcome is unknown.
Define the caller, payload, atomic claim and replay policy so a retry has a predictable application result.
Compare RESTRICT, CASCADE and SET NULL using a small isolated example, then check the actual constraint and dependent rows before changing real data.
Select one whole event per account with an explicit tie-breaker and a deliberate policy for missing timestamps.
Use ON CONFLICT to atomically insert or update rows based on unique constraints. Specify the exact conflict target, handle duplicate input rows, and understand that excluded values replace, not accumulate.
Prediction intervals aim to contain future individual observations, while confidence intervals target a mean or other parameter, and the two are not interchangeable. Empirical coverage is the held-out fraction inside the interval and width is the distance between quantile limits; a constructed five-value example gives 80 percent coverage and mean width 1.8.
Distinguish missing and empty settings, parse ports and booleans explicitly, and report configuration errors without exposing secrets.
Use ISO timestamp strings and decimal strings, then reconstruct the fields explicitly. A complete standard-library example shows timezone checks, precision and predictable failures.
This article explains how to choose effective chunk boundaries and overlap in retrieval-augmented generation (RAG) systems, comparing fixed-size and document-aware strategies. Using a hypothetical support note example, it demonstrates the risks of splitting across semantic units and shows how metadata and overlap can preserve context. The guidance is based on Microsoft Azure documentation for chunking in vector search and RAG workflows.
This document outlines the appropriate correction routes for financial journal entries versus supplier invoice corrections within SAP S/4HANA, emphasizing reversal, credit memos, and cleared items, and avoiding general advice across modules.
Compare three similarity calculations on small vectors and see when normalization changes the ranking.
See why groups of different sizes give a combined mean of 18 rather than 15, with a short Python example.
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.
Use If-None-Match to revalidate cached responses and If-Match to avoid overwriting a newer representation.
Find the expensive step and distinguish estimates from measurements.
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.