Developer | CustomGPT.ai

Connect OpenAI-Compatible Tools to CustomGPT.ai’s RAG API

An OpenAI-compatible RAG API lets applications that already use the Chat Completions format send requests to a managed retrieval system grounded in approved business content. When a tool supports a configurable API base URL and Bearer token, developers can often connect it to CustomGPT.ai by changing the endpoint, API key, and project ID rather than […]

RAG as a Service: Custom GPT API for Business Data

There’s no general API that turns a GPT configured inside ChatGPT into a reusable endpoint for your own application, OpenAI is explicit about that. Most people using “custom GPT API” mean one of two things: OpenAI’s own developer API, or a managed RAG-as-a-service platform like CustomGPT.ai that handles content ingestion, retrieval, citations, and agent management […]

RAG Chunking Strategies: How to Split Documents for Better Retrieval

A RAG system does not retrieve whole documents. It retrieves chunks. That single fact makes chunking one of the highest-leverage decisions in the entire pipeline, because the way you split content decides what the retriever can find, what the model actually sees, and what the answer can cite. Get chunking right and retrieval, grounding, and […]

RAG Architecture Patterns: From Basic to Enterprise-Grade

RAG is not a single design. It is a family of RAG architecture patterns that combine retrieval, grounding, generation, evaluation, citations, permissions, and deployment into working systems. Many teams start with a simple retrieve-and-generate pipeline, then discover that production needs far more: ingestion, chunking, indexing, hybrid retrieval, reranking, permission-aware access, citations, evaluation, monitoring, and guardrails. […]

Production RAG: Architecture, Best Practices, and Deployment Guide

Direct Answer: What Is Production RAG? Production RAG is a retrieval system built to survive real traffic: reliable ingestion, retrieval you can measure, citations on every answer, access control, monitoring and a rollback path. A prototype answers questions. A production system answers them consistently, proves where each answer came from, and tells you when it […]

Open Source RAG Frameworks: Complete Developer Comparison Guide

Direct Answer: What Are the Best Open Source RAG Frameworks? The best open source RAG frameworks are LangChain, LlamaIndex, and Haystack, but the right choice depends on whether you need workflow orchestration, data indexing, production pipelines, or a faster managed deployment. LangChain is strongest for orchestration and agents. LlamaIndex is strongest for data ingestion and […]

Legal Document RAG Systems: How Law Firms Build Source-Cited Legal AI

Quick answer: A legal document RAG system is an AI assistant that answers questions by first retrieving relevant passages from your legal documents, then generating a response grounded in those passages with citations. It gives law firms and legal teams source-cited legal AI instead of unverifiable answers from a model’s general training. Legal teams comparing […]

Custom RAG Solutions: Build vs Buy Guide for Enterprise AI

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, a custom Retrieval-Augmented Generation system looks up approved content first, then writes a response grounded […]

How to Fine-Tune ChatGPT & When to Skip It: A Comprehensive Guide

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 analysis. These models can be fine-tuned to perform specific tasks, such as customer support, content […]

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. Reranking works best when retrieval starts with useful candidates, so review chunking choices before reranking to improve the first-stage search set. Initial retrieval casts a wide net, returning documents that are semantically […]