Enhancing LLM decision-making: integrating language agent tree search with GPT-4o for superior problem-solving Image by the author: midjourney — abstract puzzleLarge Language Models (LLMs) have demonstrated exceptional abilities in performing natural language tasks that involve complex reasoning. As a result, these models have evolved to function as agents capable of planning, strategising, and solving complex…

I am now bringing you a Data Science in Marketing — Customer Base Segmentation (clustering) project. This is a high-level project that can be applied in any company that needs to understand the differences among its customers. Know that companies are eager 👹 to understand who their customers are. Or do you think all customers…

Unlocking the power of large language models Photo by ZHENYU LUO on UnsplashIn this article, I will examine how large language models (LLMs) can convert natural language into SQL, making query writing more accessible to non-technical users. The discussion will include practical examples that showcase the ease of developing LLM-based solutions. We’ll also cover various…

User Persona based Personalization of LLM generated Responses ChatGPT, or the underlying large language models (LLMs) today, are able to generate contextualized responses given a prompt. As a next step in the LLM evolution, we expect the responses to be more and more personalized with respect to the persona, conversation history, current conversation context and…

Over the past year and a half, I’ve been telling everyone I know about the potential of AI, especially with Large Language Models (LLMs). It’s time for everyone, regardless of their technical background, to learn the basics of LLMs and how to use them efficiently. In the 1960s we had the Moon. Today, we have…

Adding evaluation, automated data pulling, and other improvements. From Film Search to Rosebud 🌹. Image from Unsplash.Table of Contents Introduction Offline Evaluation Online Evaluation Automated Data Pulling with Prefect Summary Relevant Links A few months ago, I released the Film Search app, a Retrieval-Augmented Generation (RAG) application designed to recommend films based on user queries.…

Managing data models at scale is a common challenge for data teams using dbt (data build tool). Initially, teams often start with simple models that are easy to manage and deploy. However, as the volume of data grows and business needs evolve, the complexity of these models increases. This progression often leads to a monolithic…

AI for fun and profit! Photo by 🇸🇮 Janko Ferlič on UnsplashIn this article, we’ll explore how to leverage large language models (LLMs) to search and scientific papers from PubMed Open Access Subset, a free resource for accessing biomedical and life sciences literature. We’ll use Retrieval-Augmented Generation, RAG, to search our digital library. AWS Bedrock…

While we ingest the data from the API, we will apply some criteria. First, we will only ingest documents where the year is between 2016 and 2022. We want fairly recent language as terms and taxonomy of certain subjects can change over long periods of time. We will also add key terms and conduct multiple…

Intelligently synergizing dynamic programming and Monte Carlo algorithms R einforcement learning is a domain in machine learning that introduces the concept of an agent learning optimal strategies in complex environments. The agent learns from its actions, which result in rewards, based on the environment’s state. Reinforcement learning is a challenging topic and differs significantly from…