LLMs

A primer on what large language models are, why they are used, the different types, and what the future may hold for LLM applications.

In the LLM app stack, a playground is where developers can test out (and deploy) prompts. We discussed this new concept with Humanloop's CEO.

Web3 failed to remake the developer ecosystem, but the emerging LLM stack is forcing the cloud native era to adapt. We examine its progress.

To train a large language model, you must overcome three big challenges: data, hardware and legal. It helps to be a large organization, too.

LLMs hold the key to generative AI, but some are more suited than others to specific tasks. Here's a guide to the five most powerful and how to use them.

A successful first use of GPT-4 Code Interpreter raises the hope that LLMs can help democratize scripting.

🤖 What are LLMs? How can you use them? And why should you care?

In the 20th episode of my Mastodon series I pivoted to a new topic: LLM-assisted coding. After three posts in the new series, it got picked up by The New Stack. Here’s the full list so far, I…

LLMs can be more useful and less prone to hallucination when they’re able to read relevant documents, webpages, and prior conversations before responding to a new user question.

We take a look at LlamaIndex, which allows you to combine your own custom data with an LLM — without using fine-tuning or overly long prompts.

LMQL is a query language for large language models (LLMs). It facilitates LLM interaction by combining the benefits of natural language prompting with the expressiveness of Python.

The new era of tech seems to be built on superstitious behaviour

Containers, large language models (LLMs), and GPUs provide a foundation for developers to build services for what Nvidia CEO Jensen Huang describes as an "AI Factory."

At QCon New York earlier this month, two OpenAI engineers demonstrated ChatGPT's newest feature, Functions, in one session. Another talk, however, pointed to the inherent limitations of LLMs.

Recently I’ve been chatting with a number of companies who are building out internal LLM labs/tools for their teams to make it easy to test LLMs against their internal usecases. I wanted to take a couple hours to see how far I could get using Streamlit to build out a personal LLM lab for a few usecases of my own. See code on lethain/llm-explorer. Altogether, I was impressed with how usable Streamlit is, and was able to build two useful tools in this timeframe:

What role does Hugging Face play in the generative AI developer ecosystem? We take a look at the company's savvy open source branding.

Last week I had to convert a table in a Google Doc to a JSON structure that will render as an HTML page. This is the sort of mundane task that burns staggering amounts of information workers’…

We’ll use the fast-rising LLM application framework for a practical example of how to use a GPT to help answer a question from a PDF document.

Curated collection of prompts for use with LLMs. Accessible interactively in Chat Notebooks and programmatically in functions like LLMFunction. Initial categories in the Prompt Repository cover personas, functions, modifiers.