Practical guides to AI, Python, Git, SQL, Web/API, SAP/ERP and mathematical models, with explanations, code examples, sources and limitations.
Every word must appear in the article. Use quotes for an exact phrase.
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.
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.
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.
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.
Compare RESTRICT, CASCADE and SET NULL using a small isolated example, then check the actual constraint and dependent rows before changing real data.
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 a named logger, configure the application once, and attach safe context without duplicating every error.
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.
A citation identifier only proves a source was retrieved, not that it supports the claim. This article shows how to decompose answers, locate supporting passages, and separate retrieval checks from groundedness checks using a hypothetical policy example.
A posting rejection is usually a period-control issue, not a document-date mistake. The journal entry date is the document issue date, while the posting date determines the posting period that the system checks. The posting period variant, not the fiscal year variant, opens and closes periods for the header and for each account type.
Trace the purchase-order item, distinguish quantity from value, and explain a simplified 40-unit difference.
Compare three similarity calculations on small vectors and see when normalization changes the ranking.
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.
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 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.
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.
Find the expensive step and distinguish estimates from measurements.