In this article, you will learn what Python descriptors are and how to use them to build three types of controlled attributes: validated, computed, and lazily loaded.
Author Archive | Iván Palomares Carrascosa
Building a RAG Pipeline with LLM Embeddings and Scikit-learn
In this article, you will learn how to build a lightweight, end-to-end RAG pipeline using scikit-learn for vector retrieval and open-source models for embeddings and text generation, without the use of paid APIs or heavyweight frameworks.
Evaluating Graph-RAG vs. Standard RAG: A Hallucination Benchmark on Fact-Dense Queries
In this article, you will learn how to benchmark a deterministic 3-Tiered Graph-RAG system against a standard vector RAG pipeline on fact-dense queries, and what the results reveal about matching prompt complexity to model capacity.
Synchronous vs. Asynchronous Agent Execution: Architecture Patterns for Production
In this article, you will learn how synchronous and asynchronous execution patterns differ architecturally, and how to choose between them when deploying LLM-based agents to production.
Adding Temporal Reasoning to Graph-RAG: Tracking Fact Freshness and Staleness
In this article, you will learn how to add a lightweight temporal reasoning layer to a Graph-RAG system so that it can distinguish fresh facts from stale ones.
Automating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM
In this article, you will learn how to automatically extract structured knowledge from raw text and populate a knowledge graph with SPOC quads using a local LLM via Ollama.
Monitoring Embedding Drift in Production Scikit-LLM Pipelines
In this article, you will learn what embedding drift is, why it matters for production large language models, and how to implement two practical techniques to detect it.
Multilingual Text Classification with Scikit-LLM and Multilingual Embeddings
Learn how to build a multilingual text classification pipeline using multilingual LLM embeddings and Scikit-learn, without training separate models for each language.
Treating Prompt Templates as Hyperparameters in Scikit-LLM GridSearchCV
In this article, you will learn how to treat prompt templates as tunable hyperparameters for a language model, using scikit-learn’s grid search to find the best-performing prompt for a zero-shot text classification task.
Versioning and Tracking Scikit-LLM Experiments
In this article, you will learn how to build, track, compare, and register scikit-learn pipelines that integrate large language models using Scikit-LLM and MLflow.