The AI landscape is crowded with platforms claiming to offer platform integrations. But when you look closer, as many startup teams do, there’s a fundamental difference in what these platforms actually let you do. While most stop at internal tools […]
Building proof-of-concept Retrieval-Augmented Generation RAG API applications takes days. As a RAG application moves from prototype to production, production RAG chunking patterns help keep retrieval accurate as document volume grows. Scaling it to handle millions of queries while maintaining sub-second […]
Quick Answers Your AI agent answers questions perfectly… but your customers want more. They want their meeting booked. Their order tracked. Their ticket created. Right now. In the same conversation. Every “I’ll do that for you” that becomes “Please do […]
TL;DR Developer teams building RAG applications face specific integration challenges that traditional AI implementations don’t address. For scaling beyond integration patterns, see scaling RAG API applications. For the retrieval-quality layer that sits after first-pass search, see our guide to RAG […]
TL;DR Production RAG API implementation requires battle-tested patterns for security, scalability, and reliability. For deeper production scaling guidance, use the production RAG API applications guide. For the retrieval-quality layer that sits after first-pass search, see our guide to RAG reranking […]
TL;DR This guide demonstrates how to build production-ready RAG applications using proven implementation patterns from real-world deployments. Instead of theoretical examples, we’ll use actual code from the CustomGPT Starter Kit and working examples from the CustomGPT Cookbook. You’ll learn to […]
TL;DR This hands-on tutorial walks you through getting started with RAG APIs and building your first RAG-powered application from scratch. You’ll learn to set up a RAG API, upload your data sources, create a functional chat interface, and deploy a […]
TL;DR The choice between RAG API vs traditional LLM APIs comes down to accuracy, currency, and domain expertise. Traditional LLM APIs like OpenAI’s GPT-4 provide broad general knowledge but suffer from knowledge cutoffs, hallucinations, and lack of domain-specific expertise. RAG […]
If you need the lower-level stack view, the RAG system components guide walks through retrieval, augmentation, and generation. When you are ready to move past the basics, read RAG API applications in production. When building against a RAG API, review […]
TL;DR RAG APIs combine the power of large language models with your own data sources to create AI applications that are accurate, current, and contextually relevant. For the retrieval-quality layer that sits after first-pass search, see our guide to RAG […]
