Agentic RAG

I created an automation that builds a multimodal Retrieval-Augmented Generation (RAG) agent capable of indexing and analyzing complex PDFs containing text, images, and tables at scale. This agent goes beyond simply extracting text—it understands images using an AI vision model and responds to queries with images and tables embedded directly in its answers. This approach … Read more

Setting up a basic AI agent in n8n that queries a vector store is straightforward. However, ensuring that the agent consistently delivers relevant, high-quality answers takes some advanced techniques. One of the easiest and most effective ways to improve the accuracy of your retrieval-augmented generation (RAG) agent is by using re rankers. With the release … Read more

NotebookLM stands out as one of the most powerful AI research tools available today. What makes it remarkable is its ability to ground responses exclusively in the sources you provide, ensuring accuracy and relevance. However, its closed nature limits customization and self-hosting, which can be a barrier for businesses wanting a tailored AI research assistant. … Read more

When building AI agents that rely on vector stores to fetch information from your own data, you might notice that the answers aren’t always accurate. The challenge often lies in how vector search handles queries. While it excels at understanding the meaning behind natural language queries, it can struggle with specific names, acronyms, codes, or … Read more

I created an automation that simplifies the process of building Retrieval-Augmented Generation (RAG) systems using n8n and Supabase. This guide will walk you through each step, from understanding the basics to implementing advanced techniques, ensuring you can create a fully functional RAG system. Understanding RAG RAG combines traditional search methods with modern AI capabilities to … Read more

In this blog, I explore the ‘Lost Context Problem’ that often plagues RAG systems and present two innovative techniques: Late Chunking and Contextual Retrieval. These methods can significantly enhance the accuracy of your retrieval systems and minimize frustrating hallucinations. The Lost Context Problem The lost context problem is a significant challenge in retrieval-augmented generation (RAG) … Read more

In this blog, I explore the revolutionary Agentic RAG blogging system I developed using N8N. This approach overcomes traditional limitations, enabling AI agents to seamlessly gather and synthesize information from multiple data sources, resulting in more accurate and engaging content. Demo of Agentic RAG In this section, I’ll walk you through a live demo of … Read more

In this blog, I explore Cache-Augmented Generation (CAG), a groundbreaking retrieval method that taps into server-side memory from OpenAI, Anthropic, and Google Gemini. I compare it with traditional Retrieval-Augmented Generation (RAG) to determine which method is more efficient and effective for various applications. Introduction to CAG and RAG Cache-Augmented Generation (CAG) and Retrieval-Augmented Generation (RAG) … Read more