Artificial Intelligence | News, analysis, features, how-tos, and videos
Deploying a large language model on your own system can be surprisingly simple—if you have the right tools. Here’s how to use LLMs like Meta’s new Llama 3 on your desktop.
How to implement a local RAG system using LangChain, SQLite-vss, Ollama, and Meta’s Llama 2 large language model.
Companies investing in generative AI find that testing and quality assurance are two of the most critical areas for improvement. Here are four strategies for testing LLMs embedded in generative AI apps.
Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.
RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain.
Learn how to build and deploy a machine-learning data model in a Java-based production environment using Weka, Docker, and REST.
Set up a supervised learning project, then develop and train your first prediction function using gradient descent in Java.
We can dramatically increase the accuracy of a large language model by providing it with context from custom data sources. LangChain makes this integration easy.
Get a hands-on introduction to generative AI with these Python-based coding projects using OpenAI, LangChain, Matplotlib, SQLAlchemy, Gradio, Streamlit, and more.
The advantages of LangChain are clean and simple code and the ability to swap models with minimal changes. Let’s try LangChain with the PaLM 2 large language model.