LLMs

Prompt engineering technique helps large language models (LLMs) handle pronouns and other complex coreferences in retrieval augmented generation (RAG) systems.

tl;dr: OpenAI leaked AI breakthrough called Q*, acing grade-school math. It is hypothesized combination of Q-learning and A*. It was then refuted. De…

It allows a user to be served with a personalized adapter while enhancing the LLM's response by adding recent data as context.

Humane finally actually announced their first product: Ai Pin. Here's their introduction video. I love technology, and I think I'm generally enthusiastic about new tech that pushes things forwards. I'm enthusiastic about the ways LLMs are creating so many new things that weren't even possible literally one year ago, and

First we fixed a bug in an SQL query. Then we rethought the design of the query. Here are further ways to use LLMs to adjust your SQL queries.

How the tech news aggregator daily.dev created an AI search, including using an LLM Gateway to streamline and optimize interactions with LLMs.

Here’s number 12 in the series on LLM-assisted coding over at The New Stack: Let’s Talk: Conversational Software Development I keep coming back to the theme of the first article in this serie…

Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.

Explore how LLMs generate text, why they sometimes hallucinate information, and the ethical implications surrounding their incredible capabilities.

Asking LLMs to write code is a life-changer, but so is talking to them about the process. Jon Udell continues to explore LLMs for coders.

In his TED Talk, Stephen Wolfram covers the emergence of space by the application of computational rules to spacetime, gravity and quantum mechanics to AI and LLMs. Computational irreducibility and the ruliad.

Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

Microsoft researchers are introducing AutoGen, a framework for simplifying the orchestration, optimization, and automation of workflows for large language model (LLM) applications—potentially transforming and extending what LLMs can do. Learn more.

Open source UI visual tool to build your customized LLM flow using Langchain

Use LangSmith to debug, test, evaluate, and monitor chains and intelligent agents in LangChain and other LLM applications.

There are ways to manage the non-deterministic nature of LLMs. Each of these approaches has its own trade-offs.

Critics of LLM-based products like ChatGPT, Claude, Midjourney, and other such products like to brush them off as just this year’s version of NFTs. They’re crypto bullshit being peddled by the same jokers who are just out there to stow disinformation and make a quick buck. I won’

Some explanations should be written by code authors. Others may best be generated on the fly by LLM-assisted code readers.

There are many benefits of running a private LLM for your company or product, but it all boils down to being able to provide real-time data in context.

ChatGPT and LLMs can do anything, so what can you do with them? How do you know? Do we move to chat bots as a magical general-purpose interface, or do we unbundle them back into single-purpose software?

The open source LangStream framework combines data streaming with generative AI. We speak with project lead Chris Bartholomew from DataStax.

Jon Udell uses ChatGPT, Cody and GitHub Copilot to help him build an ODBC Plugin for Steampipe, an extensible SQL interface to cloud APIs.

AI presents challenges for data privacy in Large Language Models (LLMs) like ChatGPT emphasizing the need for robust security measures.

LLM Guard is a toolkit designed to fortify the security of Large Language Models, and it's freely available for usage with various LLMs.

As websites start to block Common Crawl, and as the project leans in to its role in training LLMs, will it become harder to use data from the web for other purposes?

Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models.

A paper has shown that a compression algorithm – gzip – outperforms some large language models (LLMs) in some tasks. This has the NLP community …

LLMs can deliver just-in-time knowledge tailored to real programming tasks, making it a great way to learn about coding idioms and libraries.