A practical guide to LlamaIndex, the data framework for building retrieval-augmented generation (RAG) applications that connect custom data sources to LLMs.
Why XGBoost remains the default for tabular machine learning: a practical guide with early stopping, GPU training, and SHAP explainability, plus an honest comparison with LightGBM, CatBoost, and neural networks.
A practical guide to LangChain, the framework for building LLM-powered applications using composable chains, prompt templates, and the LangChain Expression Language.
A practical guide to CatBoost, Yandex’s gradient boosting library that handles categorical features natively without manual encoding.