Reinforcement finetuning uses reward signals to guide the large language model toward desirable behavior. This method sharpens the model’s ability to produce logical and structured outputs by reinforcing correct responses. Yet, the challenge persists in ensuring that these models also…

Text embedding and reranking are foundational to modern information retrieval systems, powering applications such as semantic search, recommendation systems, and retrieval-augmented generation (RAG). However, current approaches often face key challenges—particularly in achieving both high multilingual fidelity and task adaptability without…

AI agents powered by LLMs show great promise for handling complex business tasks, especially in areas like Customer Relationship Management (CRM). However, evaluating their real-world effectiveness is challenging due to the lack of publicly available, realistic business data. Existing benchmarks…

In this tutorial, we demonstrate how to build a multi-step, intelligent query-handling agent using LangGraph and Gemini 1.5 Flash. The core idea is to structure AI reasoning as a stateful workflow, where an incoming query is passed through a series…

Web automation agents have become a growing focus in artificial intelligence, particularly due to their ability to execute human-like actions in digital environments. These agents interact with websites via Graphical User Interfaces (GUIs), mimicking human behaviors such as clicking, typing,…

Artificial Intelligence (AI) has been making significant strides over the past few years, with the emergence of Large Language Models (LLMs) marking a major milestone in its growth. With such widespread adoption, feeling left out of this revolution is not…