GenAI

This blog post presents a new modular workflow for converting PDFs and similar documents to structured data and shows you how to build end-to-end document understanding and information extraction pipelines for industry use cases.

Counting tokens is a useful task in natural language processing (NLP) that allows us to measure the length and complexity of a text. The two important use cases for counting the tokens are: controlling the length of the prompt - models has limit …

A question-based extraction approach

Presents MAG-V, a multi-agent framework that first generates a dataset of questions that mimic customer queries. It then reverse engineer alternate questions from responses to verify agent trajectories. Reports that the… — elvis (@omarsar0)

The whole point is to not let the agent do everything, but to do a… — Jerry Liu (@jerryjliu0)

— Pedro Domingos (@pmddomingos)

Learn important prompt engineering techniques to build use cases with LLMs.

Check grounding with RAG

With the advent of the Large Language Model (LLM), new possibilities dawned for metadata generation and sensitive data identification at Grab. This prompted the inception of our project aimed to integrate LLM classification into our existing data management service. Read to find out how we transformed what used to be a tedious and painstaking process to a highly efficient system and how it has empowered the teams across the organisation.

Discover how QueryGPT revolutionizes SQL query generation at Uber! Learn about the cutting-edge AI that turns natural language prompts into efficient SQL queries, boosting productivity at Uber. Dive into our journey of innovation and transformation.

A step-by-step guide with my learnings from 30+ AI implementations.

Code with the speed and knowledge of the best programmer you know. Aide is by your side.

A comprehensive collection of Large Language Model (LLM) resources, tools, and learning materials.

Entity resolution, the process of determining which digital descriptions correspond to the same real-world entities, is an important graph use case. It is also a crucial precursor to many graph data science projects. In this session, you will learn steps that the Neo4j professional services team has used in many entity resolution projects. The steps include designing a graph data model that highlights shared identifiers, standardizing the format of node properties, identifying outlier nodes that should be excluded from the matching process, using graph data science algorithms to identify duplicate entities, using string similarity to identify misspellings, and capturing the results of entity resolution in your graph. Get certified with GraphAcademy: https://dev.neo4j.com/learngraph Neo4j AuraDB https://dev.neo4j.com/auradb Knowledge Graph Builder https://dev.neo4j.com/KGBuilder Neo4j GenAI https://dev.neo4j.com/graphrag

LangGraph is a multi-agent framework. This means not only interacting with other LangGraph agents, but all other types of agents as well, regardless of how they are built. Today we are taking a few steps to to build towards this vision. We are announcing: * Agent Protocol: a common interface for

The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI

Rig: Build modular and scalable LLM Applications in Rust. Unified LLM interface, Rust-powered performance, and advanced AI workflow abstractions for efficient development.

I just wanted to validate if it's ok to initialize/compile the graph once and then use it to serve multiple parallel requests in a web application. In other words is the shared state passed fro...

Learn all you need to implement streaming in production using SSE and how to handle streaming errors.

How Codeium went from 0 to $10m in ten months, What enterpriseready.io got wrong. A comprehensive braindump on how to be Enterprise Infra Native!

Use Prompt Canvas in LangSmith to collaborate with an AI agent to build and optimize your prompts.

AI engineering from first principles

Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.

Learn how to overcome them using knowledge graphs and structured tools