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👇🏻Access Your 1000+ ChatGPT Marketing Prompts Handbook here 👇🏻

Anthropomorphic Views of LLMs and AGI Hopes

In this example, we’ll imagine that our chatbot needs to answer questions about the content of a website. To do that, we’ll need a way to store and access that information when the chatbot generates its response.

I design, build, and operate machine learning systems that serve customers at scale. I also write about data/ML systems and career.

Large language models (LLMs) are machine learning models that can comprehend and generate human language text. Learn how LLMs work and their security risks.

Learn what machine learning is, how it differs from AI and deep learning, and why it is one of the most exciting fields in data science.

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Rerankers have been a common component of retrieval pipelines for many years. They allow us to add a final "reranking" step to our retrieval pipelines — like...

Large Language Models (LLM's) are starting to revolutionize how users can search for, interact with, and generate new content. Some recent stacks and toolkit...

Real-time coding and exploring nanoGPT with me! See detailed model explanation in Andrej Karpathy's legendary video (best GPT explanation on the internet): h...

Real-time coding and exploring nanoGPT with me! See detailed model explanation in Andrej Karpathy's legendary video (best GPT explanation on the internet): h...

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TL;DR ragas is a framework designed to assess the performance of Retrieval Augmented Generation (RAG) pipelines, a type of LLM application that uses external data to enhance its context. While there are tools to build RAG pipelines, evaluating their performance can be challenging. ragas offers tools rooted in recent research to evaluate LLM-generated text, providing insights into RAG pipeline performance. It can also be incorporated into CI/CD for ongoing performance checks.

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Dive into the world of Ragas, the go-to Retrieval-Augmented Generation (RAG) pipeline evaluator. This comprehensive guide will walk you through its features, metrics, and integrations, making you an expert in evaluating Language Models (LLMs).

Learn Deep Learning with fastai and PyTorch, 2022

Implementing RAG using Langchain

This article unveils the transformative potential of RAG and its integration with LangChain and Vector Databases.

Introduction to LLM App Development Workshop: PDF Q&A App using OpenAI, Langchain, and Chainlit - GitHub - leehanchung/llm-pdf-qa-workshop: Introduction to LLM App Development Workshop: PDF...

A library for efficient similarity search and clustering of dense vectors.

Learn how to use function calling with the GPT-35-Turbo and GPT-4 models

visualizing text splitting & chunking strategiesChunkViz .com I thought I remembered a tool to visualize text chunking, but I couldn't find it, so I built oneI didn't realize it would be so visually pleasing to tinker with4 different @LangChainAI splitters featured https://t.co/4VywxHdqAa pic.twitter.com/vN2kdIBgxd— Greg Kamradt (@GregKamradt) December 8, 2023

Generative Large Language Models like OpenAI's GPT-4, Google's PaLM 2, and Discriminative models like ImageBind are models released in 2023 that combine visu...

According to the StackOverflow Survey 2023, nearly half of professional developers use Postgres. It’s natural then that the Postgres extension for vector similarity search, pgvector, is one of the most popular options for prototyping  AI-powered applications. But how to properly use pgvector? Is it the right tool for your dataset? Do you need an exact […]