Tag: AI/ML Techniques

The

A lot (if not nearly all) of the success and progress made by many generative AI models nowadays, especially large language models (LLMs), is due to the stunning capabilities of their underlying architecture: an advanced deep learning-based architectural model called the

Machine learning models deliver real value only when they reach users, and APIs are the bridge that makes it happen.

As large language models have already become essential components of so many real-world applications, understanding how they reason and learn from prompts is critical.

A few years ago, training AI models required massive amounts of labeled data.

Generative AI continues to rapidly evolve, reshaping how industries create, operate, and engage with users.

Fine-tuning remains a cornerstone technique for adapting general-purpose pre-trained large language models (LLMs) models (also called foundation models) to serve more specialized, high-value downstream tasks, even as zero- and few-shot methods gain traction.

This post is divided into three parts; they are: • Query Expansion and Reformulation • Hybrid Retrieval: Dense and Sparse Methods • Multi-Stage Retrieval with Re-ranking One of the challenges in RAG systems is that the user’s query might not match the terminology used in the knowledge base.

Building machine learning models is an undertaking which is now within everyone’s reach.