In this article, you will learn how vector databases work, from the basic idea of similarity search to the indexing strategies that make large-scale retrieval practical.
Archive | Language Models
5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering
In this article, you will learn why large language model hallucinations happen and how to reduce them using system-level techniques that go beyond prompt engineering.
Everything You Need to Know About Recursive Language Models
In this article, you will learn what recursive language models are, why they matter for long-input reasoning, and how they differ from standard long-context prompting, retrieval, and agentic systems.
From Text to Tables: Feature Engineering with LLMs for Tabular Data
This article presents a deep dive into the full process of applying feature engineering on structured text, turning it into tabular data suitable for a machine learning model.
Build Semantic Search with LLM Embeddings
In this article, you will learn how to build a simple semantic search engine using sentence embeddings and nearest neighbors.
Can LLM Embeddings Improve Time Series Forecasting? A Practical Feature Engineering Approach
In this article, you will learn whether incorporating large language model embeddings as engineered features can meaningfully improve time series forecasting performance.
KV Caching in LLMs: A Guide for Developers
In this article, you will learn how key-value (KV) caching eliminates redundant computation in autoregressive transformer inference to dramatically improve generation speed.
How to Combine LLM Embeddings + TF-IDF + Metadata in One Scikit-learn Pipeline
Build a whole fusion pipeline from scratch, that combines dense semantic information underlying text through LLM-generated embeddings, sparse lexical features with TF-IDF, and structured metadata signals.
Introduction to Small Language Models: The Complete Guide for 2026
Learn when small language models outperform large models while cutting AI deployment costs by 95%.
LLM Embeddings vs TF-IDF vs Bag-of-Words: Which Works Better in Scikit-learn?
An analytical and example-based comparison between three well-known text representation approaches, in the context of downstream machine learning modeling with scikit-learn.