RAG

A collection of 36 posts

RAG

How Graph-Based Retrieval (GraphRAG) Improves Information Discovery

GraphRAG improves information discovery by mapping relationships between data points, enabling deeper context and more accurate retrieval. This guide explores how graph-based retrieval enhances RAG systems for smarter, structured knowledge access.

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Jailbreak Risks in RAG-Powered LLMs: A Security Guide

RAG-powered LLMs are vulnerable to jailbreak and prompt injection attacks. This security guide outlines common risks, real-world examples, and best practices to protect your systems, ensure safe outputs, and maintain robust AI integrity.

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Creating Embedded Search APIs with RAG

Embedded search APIs powered by RAG deliver fast, context-aware results within applications. This guide covers building and integrating RAG-based APIs, enabling smarter search experiences tailored to your content and user needs.

codebase

Building a Codebase Exploration Tool Using RAG

RAG can power smarter codebase exploration tools. This guide shows how to implement RAG to improve code search, documentation access, and developer assistance-boosting productivity and enabling efficient understanding of large codebases.

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Can DeepSeek’s SLM Approach Replace Traditional RAG?

Integrating recent news into RAG workflows ensures timely, relevant AI responses. This guide explores tools and techniques to fetch, preprocess, and embed up-to-date content, keeping your RAG system informed and contextually aware.

RAG

Efficient Integration of Recent News into RAG Workflows

Integrating recent news into RAG workflows ensures timely, relevant AI responses. This guide explores tools and techniques to fetch, preprocess, and embed up-to-date content, keeping your RAG system informed and contextually aware.

Mathematical PDFs

Enhancing Mathematical Capabilities in RAG PDF Applications

Boosting mathematical capabilities in RAG PDF applications requires precise formula extraction and interpretation. This guide explores tools and strategies to enhance math parsing, improve retrieval, and deliver accurate, structured responses in AI systems.

Knowledge Graphs

Incorporating Knowledge Graphs in Retrieval-Augmented Generation

Integrating knowledge graphs with RAG enhances context, structure, and precision in AI responses. This guide covers techniques and best practices for combining graph-based data with retrieval-augmented generation to improve overall system performance.

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Document Storage Strategies in RAG: Separate vs Combined with Vector DB

Choosing between separate and combined document storage with vector DBs impacts RAG performance. This guide explores pros, cons, and best practices for each approach to help you optimize retrieval speed, accuracy, and system scalability.

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Integrating Transaction Processing with Retrieval-Augmented Generation

Combining transaction processing with RAG unlocks interactive, action-driven AI systems. This guide explores integration strategies, use cases, and best practices to enable real-time updates, smarter workflows, and more capable RAG applications.

RAG

Implementing Hybrid Retrieval (BM25 + FAISS) in RAG

Hybrid retrieval combines BM25 and FAISS to enhance RAG performance. This guide explores implementation strategies, benefits, and best practices to optimize search accuracy, improve retrieval efficiency, and ensure more relevant AI-generated responses.

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Evaluating RAG Quality: Best Practices for QA Dataset Creation

Quality evaluation is key for optimizing RAG systems. This guide covers best practices for QA dataset creation, helping improve retrieval accuracy, relevance, and AI-generated responses to ensure high-quality knowledge retrieval and performance.

RAG

Comparing Local LLMs vs. OpenAI for RAG: Accuracy and Cost

Local LLMs and OpenAI offer different advantages for RAG. This guide compares accuracy, cost, and performance to help you decide the best approach for retrieval-augmented generation, balancing efficiency, affordability, and scalability.

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Connecting RAG to SQL Databases: Practical Guide

Integrating RAG with SQL databases enhances data retrieval and processing. This guide covers practical steps, best practices, and optimization techniques to ensure seamless connectivity between retrieval-augmented generation systems and structured databases.

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Excel and RAG: Implementing Effective Retrieval Strategies for Spreadsheets

Enhancing retrieval from spreadsheets is key to optimizing data extraction. This guide explores effective strategies, query handling techniques, and AI-driven approaches to improve accuracy, efficiency, and structured data retrieval in RAG systems.

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The Most Common Reasons Why Your RAG System Is Underperforming

Is your RAG system underperforming? This guide explores the most common reasons, including retrieval inefficiencies, poor indexing, and context gaps. Learn how to troubleshoot and optimize your RAG system for better accuracy and performance.

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When and How to Rephrase Queries for Optimal RAG Accuracy

Rephrasing queries is key to optimizing RAG accuracy. This guide explores when and how to adjust queries for better retrieval, improved contextual understanding, and more precise AI-generated responses in knowledge-based applications.

PDF Extractor

Best PDF Extractor for RAG: LlamaParse vs Unstructured vs Vectorize

Which PDF extractor is best for RAG? This guide compares LlamaParse, Unstructured, and Vectorize, evaluating accuracy, efficiency, and integration capabilities to help you choose the right tool for AI-driven document processing and retrieval.

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Getting Started with Cache Augmented Generation in RAG

Cache-Augmented Generation boosts RAG efficiency by storing frequently accessed data for faster retrieval. This guide covers the fundamentals, benefits, and implementation strategies to enhance AI-driven knowledge retrieval and reduce latency.

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Top SOC2-Compliant Alternatives to Pinecone Assistant for RAG

Looking for SOC2-compliant alternatives to Pinecone for RAG? This guide explores top secure and scalable vector databases that ensure data privacy, enhance retrieval performance, and support AI-driven knowledge management with compliance in mind.

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Best Tools to Analyze Your RAG Knowledge Base

Analyzing your RAG knowledge base is key to optimizing retrieval and accuracy. This guide explores the best tools to evaluate performance, detect gaps, and enhance AI-driven knowledge management for more effective and reliable results.

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Agent RAG: Achieving Extreme Accuracy with Parallel Quotations

Agent RAG leverages parallel quotations to achieve extreme accuracy in retrieval. This guide explores how this technique enhances contextual understanding, improves precision, and optimizes AI-driven knowledge retrieval for better results.

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How to Get Really Good at Retrieval-Augmented Generation (RAG)

Want to get really good at RAG? This guide covers key techniques, best practices, and optimization strategies to improve retrieval-augmented generation, enhancing AI accuracy, efficiency, and contextual understanding for better performance.

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Best RAG Stack for Large Document Sets

Choosing the right RAG stack for large document sets is crucial for performance and accuracy. This guide explores the best tools, frameworks, and techniques to optimize retrieval, improve scalability, and enhance AI-driven document processing.

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Scaling RAG Systems to 20 Million Documents: Challenges and Solutions

Scaling RAG systems to 20 million documents presents challenges in retrieval speed, storage, and efficiency. This guide explores key obstacles and practical solutions to enhance performance, maintain accuracy, and optimize large-scale AI retrieval.