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Technical tutorial · RAG ticket resolution · Intermediate Build a Safer Next.js GPT-4 RAG Ticket Router Design a support-ticket routing workflow that retrieves relevant historical engineering evidence before asking a GPT-4-class model for a recommendation. This version focuses on the capabilities and risks verified in the supplied research: semantic retrieval, re-ranking, evidence-grounded generation, and protection
Tutorial Intermediate Qdrant FastAPI RAG API: Build a Grounded Service Build a retrieval-augmented generation API with Qdrant semantic search, BAAI/bge-m3 embeddings, FastAPI, LangChain orchestration, GPT-5 generation, and Pydantic response validation. What This Qdrant FastAPI RAG API Builds Retrieval-Augmented Generation, or RAG, connects a language model to an external knowledge collection. Instead of asking a model
Tutorial Intermediate ⏱ 25 min read © Gate of AI, LLC 2026 Build a LangChain Gemini File Search RAG API Learn how to design a grounded RAG workflow around Gemini API File Search, with managed retrieval, semantic document search, automatic citations, and a clear cost model. What This Tutorial Builds Retrieval-Augmented Generation, commonly called RAG,
Tutorial Intermediate Build an LLM Fine-Tuning Data Validation Pipeline Use a three-stage fail-fast workflow to check fine-tuning data, run a small validation trial, detect runtime anomalies, and preserve a separate validation set for measuring model behavior. Why Fine-Tuning Validation Must Come First Fine-tuning an LLM is a pipeline rather than a single training command. Before
Tutorial Advanced Secure MCP Tool Gateway for Next.js and OpenAI Design an execution boundary that lets an AI application discover and invoke approved tools without treating model output, MCP metadata, or internal systems as inherently trustworthy. Published by the Gate of AI Editorial & Engineering Teams. GateOfAI, LLC. What this tutorial covers Model Context Protocol,
Tutorial Intermediate Build a GPT and Claude Comparison Console in Next.js Build a local review console for placing an OpenAI GPT response and an Anthropic Claude response side by side. This verified version deliberately avoids claiming an unverified provider SDK integration. It focuses on a reproducible review workflow built around prompts, model outputs, tests, event
Architecture tutorial Intermediate © Gate of AI 2026 Build an Incident Triage Agent Architecture Design a multi-agent incident-triage workflow that enriches incidents with monitoring evidence, assigns work to the right teams, and keeps high-risk decisions reviewable by humans. Before You Build: What This Tutorial Verifies This tutorial explains a verified architecture for AI-assisted incident triage
Tutorial Build a Next.js Support Workflow for an OpenAI Backend Create a focused customer-support intake and review interface with Next.js App Router, TypeScript, and a server route boundary that is ready for a separately verified AI service. By the Gate of AI Editorial & Engineering Teams, GateOfAI, LLC. What this verified tutorial builds This tutorial
Adaptive Python AI Tutor with FastAPI and SQLite Build a focused API that accepts a Python exercise submission, requests structured tutoring feedback, stores topic mastery in SQLite, and returns a validated response to a client. What You Will Build This tutorial creates PyMentor, a small adaptive tutoring API for Python practice. A client sends a
Build a TypeScript console where OpenAI proposes a constrained incident workflow, an operator reviews the exact model context, and the server executes only an explicitly approved stored plan. What You Will Build This tutorial creates a small Next.js App Router application for human-approved workflow automation. An operator supplies incident details and chooses which runbook notes
Build a local, dependency-free sentence-window retrieval prototype, understand why precise retrieval needs surrounding context, and evaluate the evidence before connecting the pattern to a production RAG stack. What this tutorial covers Retrieval-augmented generation, usually shortened to RAG, gives an answer system external text to consult at query time. A common implementation choice is to split
Tutorial Advanced Gate of AI Editorial & Engineering Teams Learn the verified design principles behind PostgreSQL-native, structure-aware Graph RAG: keep searchable chunks, canonical entities, relations, and time-aware evidence in one database. What This PostgreSQL pgvector Graph RAG Tutorial Covers Retrieval-augmented generation is often introduced as a simple pipeline: split documents into chunks, embed the chunks,