MongoDB
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GenAI
Tutorial
Create a Neo4j GraphRAG Workflow Using LangChain and LangGraph
Create a Neo4j GraphRAG workflow using LangChain and LangGraph, combining graph queries, vector search, and dynamic prompting for advanced RAG.
In this situation, we need to parse the question into the desired number of subqueries that perform a necessary task.
GraphRAG
Low-Hanging Fruit for RAG Search - jxnl.co
Explore low-hanging fruit strategies to enhance your RAG search systems and improve user experience with practical techniques.
RAGUsing LangGraph and Graphiti — Zep Documentation
Building an agent with LangChain's LangGraph and Graphiti
Use CasesDatabricks AI Security Framework (DASF)
An actionable framework for managing AI security
Security
Security planning for LLM-based applications
This article discusses the Security planning for the sample Retail-mart application. It shows the architecture and data flow diagram of the example application.
LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain
What is Databricks Feature Serving? - Azure Databricks
Feature Serving provides structured data for RAG applications and makes data in the Databricks platform available to applications deployed outside of Databricks.
With Databricks Feature Serving, you can serve structured data for retrieval augmented generation (RAG) applications, as well as features that are required for other applications, such as models served outside of Databricks or any other application that requires features based on data in Unity Catalog.
Databricks Certified Generative AI Engineer Associate
Neo4jLangfuse
Open source LLM engineering platform - LLM observability, metrics, evaluations, prompt management.
Products
UX considerations for generative AI apps and agents | Google Cloud Blog
We created a collection of user-tested micro-interaction design patterns and research-backed guidance for building generative AI applications.
UX/UI
Redesigning How We Work at Microsoft with Generative AI
Learn how we’re using generative AI to transform our approach to UX design and to rethink how our teams create products together.
The RAG Playbook - jxnl.co
Discover a systematic approach to enhance Retrieval-Augmented Generation (RAG) systems for improved performance and user satisfaction.
Everything You Need To Build Agent-Native Applications (with LangGraph and CoAgents)
CoAgents, the building blocks for the next generation of AI-native apps.
LangGraphA new wave of AI apps with agent-native UX is emerging, from Replit Agent to v0. Using LangGraph + 's new CoAgents extension, developers can build agent-native React applications.
In CopilotKit's blog, see how to use:
• Real-time state sharing to match user…
— LangChain (@LangChainAI)
The landscape of LLM guardrails: intervention levels and techniques
Explore the techniques to build guardrails for Large Language Models (LLMs) to ensure safe, reliable, and accurate outputs. Learn about rule-based methods, LLM metrics, LLM judges, and prompt engineering.
Solutions
Lamini - Enterprise LLM Platform
Lamini is the enterprise LLM platform for existing software teams to quickly develop and control their own LLMs. Lamini has built-in best practices for specializing LLMs on billions of proprietary documents to improve performance, reduce hallucinations, offer citations, and ensure safety. Lamini can be installed on-premise or on clouds securely. Thanks to the partnership with AMD, Lamini is the only platform for running LLMs on AMD GPUs and scaling to thousands with confidence. Lamini is now used by Fortune 500 enterprises and top AI startups.
Hierarchical Indices: Enhancing RAG Systems
Hello, AI and data professionals! Today, we’re exploring hierarchical indices — a method significantly improving information retrieval in…
I've been building agents for almost 1.5 years and can confidently 99% of the "ai browsing" demos are useless.
the reality is consumers won't have millions of AI agents working 24/7 for them, bargaining and shopping to save $20. there will just be an ai shopping app.
compute…
— Sully (@SullyOmarr)
Agents
GraphRAG Analysis, Part 2: Graph Creation and Retrieval vs Vector Database Retrieval - Blog | MLOps Community
GraphRAG (by way of Neo4j in this case) enhances faithfulness (a RAGAS metric most similar to precision) when compared to vector-based RAG, but does not significantly lift other RAGAS metrics related to retrieval; may not offer enough ROI to justify the hype of the accuracy benefits given the performance overhead.
How to build a terrible RAG system - jxnl.co
Explore inverted thinking to build a terrible RAG system and learn pitfalls to avoid for better recommendations.
CalPitch: Enhancing BD with Llamaindex + GPT-4o - Calsoft AI
CalPitch is our AI-powered sales outreach tool designed to transform how our business development team connects with prospects through cutting-edge technology.
UX for Agents, Part 2: Ambient
This is our second post focused on UX for agents. We discuss ambient background agents, which can handle multiple tasks at the same time, and how they can be used in your workflow.
UX for Agents, Part 1: Chat
At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. Check out that talk here. In this post I will dive deeper into UX for agents. Thanks to Nuno Campos, LangChain founding engineer for many of the original thoughts and analogies
Memory for agents
At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. Check out that talk here. In this post I will dive more into memory. See the previous post on planning here, and the previous posts on UX here, here, and here.