Introduction Custom RAG solutions are AI systems that connect a language model to a company’s own knowledge sources so the model can retrieve relevant information before generating an answer. Instead of relying only on what a model learned during training, […]
Introduction To connect an AI tool to a CustomGPT.ai hosted MCP server, open your CustomGPT.ai project, copy your Project ID and MCP token, then add the hosted MCP server URL or JSON configuration to your AI tool’s MCP settings. The […]
OpenAI’s Generative Pre-trained Transformer (GPT) models have reshaped the natural language processing (NLP) field with their remarkable capabilities in understanding and generating human-like text. These models have found applications across various domains, from chatbots and content creation to complex data […]
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 […]
Chunking is only one part of the pipeline; the technical RAG component guide shows how retrieval, augmentation, and generation fit together. TL;DR Document chunking is the foundation of every RAG system, yet it’s often treated as an afterthought. The wrong […]
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. When RAG is the better choice, use this practical guide to implementing retrieval-augmented […]
