GenAI

Building and Deploying GenAI GraphRAG Applications and AI agents using Google Cloud’s Vertex AI Reasoning Engine with LangChain and Neo4j Authors: Michael Hunger,  Head of Product Innovation, Neo4j Maruti C, Partner Engineering Lead,Google Generative AI developers not familiar with orchestration too...

Learn how to add domain specific vocabulary to your RAG with Agents using LangChain and LangGraph and make smarter and more useful RAG.

Use the GraphRAG Python package to transform unstructured data into knowledge graphs and enhance GenAI retrieval accuracy and relevance.

In this informative video, we dive into the world of designing user experiences (UX) for AI applications. Bethany Jepchumba explores the importance of building trust and transparency in AI systems to ensure user satisfaction and Responsible AI. In this video, we cover: Introduction to User Experience and Understanding User Needs. Designing AI Applications for Trust and Transparency. Designing AI Applications for Collaboration and Feedback. Recommended resources The full "Generative AI for Beginners" Course After completing this lesson, check out our Generative AI Learning collection to continue leveling up your Generative AI knowledge! Best practices for building collaborative UX with Human-AI partnership Introduction to guidelines for human-AI interaction Related episodes Generative AI for Beginners

Best practices for designing AI user experiences What are the Guidelines? The Guidelines are evidence-based best practices for designing your AI user experiences. How do I use the Guidelines? The Design Library helps you learn each Guideline. Explore examples and design patterns for implementing them throughout the user experience: upon initial interaction, during interaction, when […]

Learn about the best practices for creating the collaborative user experiences by leveraging the power of human-AI partnership. Learn how to optimise your design process with these essential tips and strategies.

UX tips and recommendations for ISVs building generative AI applications

This article provides an overview of the Foundation Model APIs in Databricks. It includes requirements for use, supported models, and limitations.

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team. - openai/swarm

Open-source examples and guides for building with the OpenAI API. Browse a collection of snippets, advanced techniques and walkthroughs. Share your own examples and guides.

Practical RAG techniques for engineers: learn production-ready solutions from industry experts to optimize performance, cut costs, and enhance the accuracy and relevance of your applications.

One AI engineer's journey to get great results when building an agentic RAG system.

An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.

Notes about my hobbies and other things I find interesting.

Notes about my hobbies and other things I find interesting.

Notes about my hobbies and other things I find interesting.

Floneum is a graph editor for local LLM workflows.

Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, all with different implementation methods. To mitigate this, we propose rerankers, a Python library which provides a simple, easy-to-use interface to all commonly used re-ranking approaches.

DocETL is an open-source system for building LLM-powered data processing pipelines, offering declarative operators and powerful optimization for complex document analysis tasks

Community driven roadmaps, articles and guides for developers to grow in their career.

In this tutorial, we explore and evaluate different chunking strategies to choose the right one for a sample RAG application.

This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive A...

Distinguishing problems with external vs. internal LLMs, and data vs non-data patterns

Structured input/output, prefilling, n-shots prompting, chain-of-thought, reducing hallucinations, etc.

Learn how to combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.

What’s the real reason AI hasn’t yet delivered on its hype?

Build language agents as graphs

Build resilient language agents as graphs.