GenAI

Learn how to fine-tune Cohere's reranker and generate synthetic data using DSPy!

Discover 8 powerful tools transforming prompt engineering from trial-and-error into scalable systems—featuring visual workflows, auto-tuned prompts, and memory-

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Explore how semantic chunking enhances RAG systems by improving context, precision, and performance through optimized chunking strategies and advanced tools.

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont...

Learn how to create and configure model serving endpoints that serve custom models.

My step-by-step approach for building and evolving Agentic Systems that work.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents

Take a deep dive into Neo4j Aura Graph Analytics, and learn how to run PageRank, community detection, and other algorithms directly in your workflow.

Knowledge graphs are powerful tools for representing relationships between entities in a structured format. They are widely used in various…

A comprehensive guide to building a GenAI app using a HuggingFace model, MLflow, Unity Catalog and Databricks Apps, covering setup, development, and deployment.

It’s been a while since I last wrote a blog post, and I’m excited to be back!

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

In this blog post we will build a local Deep research agent using Qwen 3 8b model, LangGraph, Composio, and Ollama.

An example showing how A2A and MCP can be used together - ishanExtreme/a2a_mcp-example

Visual RAG expands AI's ability to understand and utilize charts, graphs, and images, a critical skill as 65% of people are visual learners. Mastering this technology allows you to build truly multimodal AI systems that can reason about visual data, giving you a competitive edge in enterprise AI development and opening new possibilities for data-driven applications.

Explore the top Retrieval-Augmented Generation (RAG) techniques of 2025, including Traditional RAG, Long RAG, Self-RAG, and more.

TL;DR Deep research is the hottest AI feature right now. Every LLM provider is adding to...

Streamline the process of working with legal contracts by implementing an end-to-end solution using agentic GraphRAG.

Vector search represents a revolution in information retrieval. Vector embedding - by taking account of context and semantic meaning - empowers vector search to return more relevant and accurate results. In this article we compare two common approaches to multi-attribute vector search.

Learn about Google's Agent2Agent (A2A), an open protocol for AI agent collaboration across different systems.

Weights & Biases, developer tools for machine learning

DeerFlow is a community-driven framework for deep research, combining language models with tools like web search, crawling, and Python execution, while contributing back to the open-source communit...

A curated list of 100+ resources for building and deploying generative AI specifically focusing on helping you become a Generative AI Data Scientist with LLMs - business-science/awesome-generative-...

Learn common agentic design patterns and workflows for building robust, scalable AI applications, understanding when to use each.