Visualizing and Evaluating RAG Systems with Ragas

📖 TUTORIALS & CASE STUDIES

Visualizing and Evaluating RAG Systems with Ragas

read time: 15 minutes
This article provides a comprehensive guide on how to use Ragas, an open-source system for evaluating Retrieval-Augmented Generation (RAG) systems. It covers the process of building a RAG system, generating questions and answers, evaluating the system, and visualizing the results using Renumics Spotlight.

Building a Chatbot with Langchain and Neo4j

read time: 45 minutes
This article from RealPython delves into building a powerful healthcare chatbot using LangChain and Neo4j for efficient data retrieval and contextual responses. Gain hands-on experience combining LLMs with graph databases to create sophisticated AI assistants.

Iterating Towards LLM Reliability with Evaluation-Driven Development

read time: 12 minutes
Dosu, an AI teammate for software development, uses Evaluation Driven Development (EDD) to ensure reliability and performance. The team at Dosu leverages LangSmith, a tool that helps monitor and evaluate Dosu's activity, to identify failure modes and improve the system. The article provides insights into the challenges and solutions in maintaining and improving LLM-based products.

🧰 TOOLS

Superopenai: A New Tool for LLM Development

read time: 10 minutes
Superopenai is a minimal convenience library designed to enhance the development process with Large Language Models (LLMs). It provides features like prompt visibility, debugging, quality-cost-speed tradeoff, and caching of identical requests. It's compatible with most third-party libraries and focuses on local development. Learn more about this tool on Github.

Fructose: A Python Package for Easy LLM Integration

read time: 8 minutes
Fructose is a Python package that simplifies the integration of Large Language Models (LLMs) into your applications. It provides a strongly-typed interface for LLM calls, supports various data types, and allows for custom prompt templates. It currently works with OpenAI, requiring your own API key. Learn more about Fructose here.

📰 NEWS & EDITORIALS

The Impact of Generative AI on Low-Code Development

read time: 15 minutes
This article explores the intersection of generative AI and low-code development. It discusses how generative AI, like GitHub Copilot, is transforming software development, potentially turning it into a manufacturing process. The article also debates whether AI code generators will replace low-code platforms, and how these technologies might shape future developer skillsets and software quality.

Meet Devin: The World's First Fully Autonomous AI Software Engineer

read time: 10 minutes
Cognition Labs introduces Devin, the world's first fully autonomous AI software engineer. Devin can execute complex tasks, learn over time, fix mistakes, and collaborate with users. It can learn new technologies, build and deploy apps, find and fix bugs, train AI models, and contribute to production repositories. Devin outperforms previous models on the SWE-bench benchmark, resolving 13.86% of issues end-to-end.

Security Vulnerabilities in AI Assistants: A Deep Dive

read time: 8 minutes
Researchers have discovered a method to decipher AI assistant responses with high accuracy, exploiting a side channel present in most AI assistants. This study reveals that an adversary monitoring data packets between an AI assistant and the user can infer the topic of 55% of all captured responses, often with high word accuracy. The findings highlight the need for improved encryption methods in AI assistant communication.