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.
Archive | Language Models
How to Fine-Tune Llama 3 for Custom Tool Calling with Unsloth in Python
In this article, you will learn how to fine-tune Llama 3 8B for custom tool calling using Unsloth and QLoRA, so that the model reliably outputs structured JSON payloads for a specific API schema.
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.
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.
RAG vs. Fine-Tuning for Domain Adaptation: When to Use Which
In this article, you will learn the mechanical difference between retrieval-augmented generation and fine-tuning, when each technique is the right tool, and how to decide which one, or both, your production system actually needs.
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.
Build And Understand a Vector Database From Scratch in 10 Easy Steps
In this article, you will learn how a vector database works under the hood by building one from scratch in ten incremental steps using Python and NumPy.
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.