LLMs

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.

In this article we explore how to combine a large language model (LLM) with a relational database to allow users to ask questions about their data in a natural way. It demonstrates a Retrieval-Augmented Generation (RAG) system built with Go that utilizes PostgreSQL and pgvector for data storage and retrieval. The provided code showcases the core functionalities. This is an overview of how the

Revisiting a documentation sprint to explore how LLM-powered tools like Unblocked can help us understand and explain complex codebases.

Researchers in Japan have effectively developed a diverse range of personality traits in dialogue AI using a large-scale language model (LLM). Using the prisoner's dilemma from game theory, Professor Takaya Arita and Associate Professor Reiji Suzuki from Nagoya University's Graduate School of Informatics' team created a framework for evolving AI agents that mimics human behavior by switching between selfish and cooperative actions, adapting its strategies through evolutionary processes. Their findings were published in Scientific Reports.

Learn how to use local LLM models to Chat with Cody without an Internet connection powered by Ollama.

We look at an open source method to run large language models locally. LocalAI is an alternative to Ollama, a private company.

Data contamination (or data leakage) in AI occurs when the data used to train the model has been "contaminated" with overlapping data.

In this article, we will look at the limitations of RAG and domain-specific Fine-tuning to adapt LLMs to existing knowledge and how a team of UC Berkeley..

We demonstrate how to combine LangChain and Google's Gemini LLM to summarize blog posts and articles on the internet.

If you're a developer needing to write a business document, Jon Udell explores the benefits (and quirks) of LLM-assisted copy editing.

A feature-rich self-hosted WebUI for LLMs, supported LLM runners include Ollama and OpenAI-compatible APIs.

Tools like Ollama let you experiment with large language models on an average PC

The promise of LLMs for the software development world is to transform coders into architects. Not all LLMs are created equal, though.