Tool use, in which an LLM is given functions it can request to call for gathering information, taking action, or manipulating data, is a key design pattern of AI agentic workflows. You may be familiar with LLM-based systems that can perform a web search or execute code. Some of
LLMS
-
Building Great RAG Solutions with AI21’s Contextual Answers
By
–
Building a RAG solution is easy. Building a great one is not. In our guest blog on @streamlit
, our team explores the intricacies of how AI21's Contextual Answers Task-Specific Model & our RAG Engine generate context-based answers grounded in your proprietary organizational data. -
LLM Plugin for Git Branch Automation Commands
By
–
I have something like that as a plugin! llm cmd git rename this branch to issue-34
-
llm-command-r 0.2 Adds Websearch Connector for Cohere Models
By
–
Here's llm-command-r version 0.2, which adds support for the websearch connector, an option which allows the Cohere models to run a web search and use the results from that as part of the context for answering the prompt https://
github.com/simonw/llm-com
mand-r/releases/tag/0.2
… -
Is AI becoming sentient? Examining predictive models and reasoning
By
–
Is AI close to being sentient? How can a model that "predicts" the next phrase have such complex reasoning capabilities. What do you think?
-
Activation Trade-offs: Current vs Future Token Optimization
By
–
I guess there is a tradeoff between how each activation is used, whether it is doing something that's optimal for a current or future token. i'm using information and store both pretty loosely. 🙂
-
Adaptive RAG Implementation with Cohere Command-R+
By
–
Adaptive RAG w/ Cohere's new Command-R+ Adaptive-RAG (
@SoyeongJeong97 et al) is a recent paper that combines (1) query analysis and (2) iterative answer construction to seamlessly handle queries of differing complexity. We took at stab at implementing these ideas from scratch -
Transformers Token Processing: Information Storage Nuances
By
–
yes! there is a lot of nuance. My preferred way to put it is "transformers don't pre-store information for future tokens at the expense of the current token" (very much)
-
Enterprise AI Applications: LLMs Customization and Inference Deep Dive
By
–
At this #GoogleCloudNext session, experts will delve into the complexities of building enterprise #AI applications, including customization, evaluation, and inference of large language models (#LLMs). Don’t miss this deep dive, register now. https://
nvda.ws/3PM0xW4 -
Paper on Pre-caching and Thinking Ahead in Language Models
By
–
oh and here's a link to the paper. includes a lot of theorems, experiments on a synthetic problem to explicitly test pre-caching (one sort of "thinking ahead") and experiments on language.