This guide explains how LIMIT and OFFSET work in PostgreSQL, their use cases for pagination, why large OFFSET values cause performance issues, and when to switch to cursor-based pagination. It includes syntax examples, mandatory ORDER BY requirements, common pitfalls, and alternative methods aligned with PostgreSQL and GitHub REST API standards.
PostgreSQL / database pagination / LIMIT / OFFSET / cursor-based pagination / query performance
A practical guide to choosing precision-oriented metrics, threshold analysis, and confusion matrix decomposition in scikit-learn for minimizing false positive rates in binary classification tasks.
binary-classification / precision / false-positive / scikit-learn / threshold-selection / confusion-matrix / det-curve / model-evaluation
This document outlines how to configure document splitting within SAP S/4HANA to enable balance sheets for multiple business segments, leveraging the Universal Journal. It details the benefits of this approach, including the elimination of reconciliation efforts and the creation of granular financial reports. We cover the key organizational elements - Chart of Accounts and Ledger Unit - and how they interact with segment reporting.
SAP S/4HANA / Universal Journal / Segment Reporting / Financial Accounting / Controlling / Document Splitting / Balance Sheets / Reporting
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.
semantic search / vector search / embeddings / AI / information retrieval / natural language processing / hybrid search / 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.
ai / precision / recall / evaluation / small dataset / machine learning / scikit-learn / classification
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.
azure-ai-search / hybrid-search / vector-search / semantic-reranker / index-schema / embeddings
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.
regression / model evaluation / MAE / RMSE / error metrics / scikit-learn / machine learning
Distinguish storage, validation and shared caching. Three response policies show when to use public max-age, private no-cache and no-store.
Cache-Control / HTTP / Caching / Web Development / Privacy / Security / Browser / CDN
Separate a request timeout from the total retry budget, interpret Retry-After, and avoid duplicating writes when the server outcome is unknown.
HTTP / Retries / Timeouts / Idempotency
Define the caller, payload, atomic claim and replay policy so a retry has a predictable application result.
web
Understand the differences between offset-based and cursor-based pagination and when to use each, especially when dealing with frequently changing datasets.
api / pagination / rest / offset / cursor / data consistency / web development
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.
postgresql / sql / upsert / insert / on-conflict / unique-constraint / concurrency / database
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.
prediction interval / confidence interval / quantile regression / empirical coverage / interval width / scikit-learn / calibration / held-out evaluation
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.
python / json / parsing / decoding / file handling / error handling / pathlib / data serialization
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.
RAG / chunking / Azure AI Search / information retrieval / document processing
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.
RAG / citation verification / groundedness / retrieval evaluation / prompt engineering / LLM evaluation
A detailed technical breakdown of the differences between WHERE and HAVING clauses, focusing on execution order, aggregate function compatibility, and performance optimization strategies.
SQL / PostgreSQL / Database / Query Optimization / Data Analysis
See why groups of different sizes give a combined mean of 18 rather than 15, with a short Python example.
math
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.
LLM / JSON / JSON Schema / Data Validation / Software Engineering / Python
Separate authentication, permissions, rate limits and service availability before retrying.
HTTP / API / Troubleshooting
Read training and validation scores together, fit preprocessing inside a pipeline and keep a final test set out of model selection.
Machine Learning / Validation / Overfitting
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.
python / csv / data-processing / encoding / utf-8-sig / automation
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.
Python / pathlib / relative paths / working directory / __file__ / os.getcwd
How retrieval connects a language model to documents, and why a citation is not a guarantee.
AI / RAG / Retrieval