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Developer

10
Oct
RAG reranking techniques
RAG Reranking Techniques: Improving Search Relevance in Production

TL;DR RAG systems often fail not because of poor embeddings or weak LLMs, but because they feed irrelevant information to the generation stage. Initial retrieval casts a wide net, returning documents that are semantically similar but not actually relevant to […]

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09
Oct
RAG Chunking Strategies
RAG Chunking Strategies: Optimizing Document Processing for Better Retrieval

TL;DR Document chunking is the foundation of every RAG system, yet it’s often treated as an afterthought. The wrong chunking strategy can cripple your RAG performance regardless of how sophisticated your embedding model or LLM is. Poor chunking leads to […]

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08
Oct
RAG Vector Database Selection
RAG Vector Database Selection: Pinecone vs Weaviate vs ChromaDB for Developers

TL;DR When building Retrieval-Augmented Generation (RAG) systems, your vector database choice fundamentally determines performance, cost, and operational complexity. Unlike traditional databases focused on exact matches, vector databases power semantic search by storing high-dimensional embeddings that capture meaning, enabling RAG systems […]

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07
Oct
RAG Evaluation Metrics
RAG Evaluation Metrics: How to Measure and Improve Your RAG System

TL;DR Building a RAG system is straightforward—building one that consistently delivers accurate, relevant responses is not. Without proper evaluation metrics, you’re flying blind, unable to distinguish between minor improvements and system-breaking regressions. Traditional NLP metrics like BLEU and ROUGE miss […]

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06
Oct
RAG vs Prompt Engineering
RAG vs Prompt Engineering: Choosing the Right AI Enhancement Strategy

TL;DR As Large Language Models (LLMs) become central to business applications, developers face a fundamental question: how do you enhance model performance for specific use cases? Two primary approaches dominate the landscape—Prompt Engineering and Retrieval-Augmented Generation (RAG)—each addressing different limitations […]

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05
Oct
RAG vs Semantic Search
RAG vs Semantic Search: Understanding the Key Differences for Developers

TL;DR Many developers entering the AI space encounter similar-sounding technologies—Retrieval-Augmented Generation (RAG) and Semantic Search—and assume they serve the same purpose. While both involve finding relevant information using advanced natural language processing, they solve fundamentally different problems and serve distinct […]

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04
Oct
RAG vs Fine-Tuning
RAG vs Fine-Tuning: When to Use Each Approach for AI Applications

TL;DR When building AI applications with Large Language Models (LLMs), developers face a critical decision: how to adapt these powerful but general-purpose models for specific business needs. Two dominant approaches have emerged—Retrieval-Augmented Generation (RAG) and fine-tuning—each with distinct advantages, limitations, […]

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03
Oct
Open Source RAG Frameworks
Open Source RAG Frameworks: Developer’s Complete Comparison Guide

TLDR The open source ecosystem for RAG development has exploded with dozens of frameworks, libraries, and tools. Each promises to make building RAG systems easier, but they take different approaches and excel in different areas. Choosing the wrong framework can […]

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02
Oct
RAG System Design
RAG System Design: From Vector Databases to API Endpoints

TLDR Designing a RAG system is like architecting a library where books can answer questions directly. You need a way to store knowledge (vector database), understand questions (embedding models), find relevant information (search algorithms), and provide answers (API endpoints). Each […]

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01
Oct
Building Production RAG Pipelines
Building Production RAG Pipelines: Architecture Best Practices

TLDR Production RAG pipelines require careful planning around data ingestion, quality control, error handling, and monitoring. Key considerations include handling large document volumes, maintaining system reliability, ensuring data freshness, and scaling to support thousands of users. CustomGPT.ai manages production complexity […]

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