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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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, […]
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 […]
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 […]
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 […]
