When it comes to AI, it seems like the vast majority of people I talk to believe large language models (LLMs) are either going to surpass human intelligence any…

With the surge of LLMs with billions of parameters like GPT4, PaLM-2, and Claude, came the need to steer their behavior in order to align them with tasks. This blog post will cover more complex state-of-the-art methods in prompt engineering including Chains and Agents, along with important concept definitions such as the distinctions between them.

Data licensing deals, scaling, human inputs, and repeating trends in open vs. closed LLMs.

Reversible computing could be critical in the LLM era. We look at how reversible architecture saves energy and what it means for developers.

As the barrier that separates data from not-data fades away, a long-envisioned calendar scenario becomes real with an LLM-backed Datasette plugin.

User-friendly WebUI for LLMs (Formerly Ollama WebUI) - open-webui/open-webui

In the realm of Large Language Models (LLMs), Daniel Miessler’s fabric project is a popular choice for collecting and integrating various LLM prompts. However, its default requirement to access the OpenAI API can lead to unexpected costs. Enter ollama, an alternative solution that allows running LLMs locally on powerful hardware like Apple Silicon chips or […]

I’m a big fan of Llama. Meta releasing their LLM open source is a net benefit for the tech...

All we have to do is get closer to the metal

We will see code interpreters powering even more AI agents and apps as a part of the new ecosystem being built around LLMs, where a code interpreter represents a crucial part of an agent’s brain.

Now that LLMs can retrieve 1 million tokens at once, how long will it be until we don’t need retrieval augmented generation for accurate AI responses?

I have very mixed opinions on LLMs, as they stand. This note won’t be digging into my thoughts there - I don’t want to have that discussion. However, while I’m not exactly doing cutting-edge research here, I do put effort into publishing for humans.

We discover that mapping one domain (publishing) into another (the domain-specific language of SQL) works heavily to an LLM's strengths.

Its data-driven matching algorithms that pair people to positions employs a structured taxonomy to overcome other models’ limitations.

Retrieval-augmented generation facilitates a radical makeover of LLMs and real-time AI environments to produce better, more accurate search results.

The evolution of software development over the past decade has been very frustrating. Little of it seems to makes sense, even to those of us who are right in the middle of it.

When designing a domain-specific enterprise-grade conversational Q&A system to answer customer questions, Conviva found an either/or approach isn’t sufficient.

Oso explains how to use a vector database and retrieval-augmented generation to lock data in LLMs to permissions and decouples authorization data and logic.

WebAssembly makes it quick and easy to download and run a complete LLM on a machine without any major setup.

Release high-quality LLM apps quickly without compromising on testing. Never be held back by the complex and subjective nature of LLM interactions.

Vector databases enable businesses to affordably and sustainably adapt generic large language models for organization-specific use.

Introduction In today’s world, LLMs are everywhere, but what exactly is an LLM and what are they used for? LLM, an acronym for Large Language Model, is an AI model developed to understand and generate human-like language. LLMs are trained on huge data sets (hence “large”) to process and generate meaningful and relevant responses based

Modeling the compute versus performance tradeoff of many open LLMs.

Some LLMs (Large Language Models) can act as useful programming assistants when provided with a project’s source code, but experimenting with this can get a little tricky if the chatbot has n…

Combining retrieval-augmented generation (RAG) with SQL makes it easier to apply LLMs to wring more insights from your company data.

Pretty much every company I know is looking for a way to benefit from Large Language Models. Even if their executives don’t see much applicability, their investors likely do, so they’re staring at the blank page nervously trying to come up with an idea. It’s straightforward to make an argument for LLMs improving internal efficiency somehow, but it’s much harder to describe a believable way that LLMs will make your product more useful to your customers.