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Latest Articles

Discover the latest insights, tutorials, and best practices in Retrieval Augmented Generation

Revolutionizing Retrieval-Augmented Generation: Introducing Rag.pro's Domain-Specific Web Retriever

Discover RAG.pro's game-changing API for building efficient RAG pipelines without the hassle of manual setup. With domain-specific web retrievers, RAG.pro allows developers to filter searches to up to three domains, ensuring precise and relevant results. Unlike traditional web search APIs, RAG.pro returns high-quality context tokens for better LLM performance, offering a perfect balance of accuracy and control over raw speed. Say goodbye to complex RAG pipelines and harness the power of RAG.pro's domain-filtered retrieval for your AI applications.

October 14, 2024

Why Episodic Data is the Correct Choice for RAG Systems in LLMs

The choice between episodic (example-based) data and informative (theory-based) data is critical. This article explores why episodic data—practical examples of tasks being done—outperforms informative data in generating higher-quality responses. By focusing on injecting relevant episodic data into prompts, you can significantly enhance the effectiveness of your RAG systems, ensuring that the output is more accurate and aligned with real-world applications.

August 18, 2024

The Challenges and Potential of RAG Systems for Documentation

Explore the challenges and potential of Retrieval-Augmented Generation (RAG) systems for documentation, including implementation hurdles and future prospects in AI-driven coding assistance.

July 31, 2024

What's going to kill your RAG business?

Operating a RAG app with your datasource being the open web is a tricky business in today's ecosystem. This post goes over the ways that your RAG business might get killed by an innovation and what innovation that might be. It's important to plan ahead for these things so you can be in a better position for when an innovation threatens your business.

July 18, 2024

Why You Should Be Using Hybrid Search in Your RAG Systems

Hybrid search combines the strengths of keyword-based and semantic search methods, offering superior search accuracy, relevance, and efficiency for Retrieval Augmented Generation (RAG) systems. Discover how hybrid search addresses the limitations of dense embeddings and enhances your RAG systems.

July 17, 2024

State of the Art RAG over Unstructured Data

An overview of a pipeline that utilizes basic Retrieval-Augmented Generation (RAG) over unstructured data, including text chunking, embedding, vector databases, and chains with a detailed example.

July 7, 2024

What is GraphRAG and Why You Should Care

Discover GraphRAG, Microsoft's groundbreaking advancement in Retrieval Augmented Generation (RAG). This innovative approach combines LLM-derived knowledge graphs with graph machine learning to enhance search relevancy and enable holistic data analysis. Learn how GraphRAG outperforms traditional RAG in answering global questions, connecting disparate information, and providing comprehensive insights from complex datasets. Explore its real-world applications, potential challenges, and why it matters for developers and businesses looking to leverage cutting-edge AI technology. Stay ahead of the curve with GraphRAG – the next evolution in AI-powered information retrieval and generation.

July 3, 2024

Will RAG be needed in the Future?

The necessity of RAG in our LLM applications has been at question ever since it was first introduced. Although RAG is not a new concept, I believe it WILL be needed for the future of LLM applications. This article will explain why I believe it this.

July 2, 2024

How RAG works

In a lot of ways, the biggest opportunity in LLMs is learning how to extend their knowledge through reliable data sources that contain the answers to the user's questions. Feeding this data into the LLM correctly, makes the LLM smarter and more reliable. RAG is how you do that. This post will describe how it works from first principles.

July 1, 2024