AI Chatbots and RAG: Guides and Tutorials
How to build chatbots that answer from your own documents and cite where the answer came from. Retrieval design, vector databases, evaluation, and what separates a demo from a support tool customers trust.
4 posts
Guide18 min readEvery AI Agent I Build Has Memory. Here Is the Exact Architecture I Use.
Most AI agents forget everything the moment a conversation ends. Here is the memory architecture I use across 126 production deployments, with real code for LangGraph, Mem0, Redis, and Zep.
Guide18 min readVector Databases for AI Agents: Which One Actually Works in Production?
A practical comparison of Pinecone, Qdrant, Weaviate, Chroma, and pgvector for AI agent workloads, with real cost breakdowns and a decision framework from 31 production deployments.
Guide21 min readAgentic RAG: The Complete Production Guide Nobody Else Wrote
A practitioner's guide to agentic RAG covering the five-component architecture, chunking strategies, four common failure modes, LLM-as-judge evaluation, and real cost-per-query numbers from 126 production deployments.
Guide14 min readWhat Is RAG? The Business Owner's Guide to AI That Actually Knows Your Company
Generic AI makes things up about your business. RAG (Retrieval Augmented Generation) connects AI to your actual documents so it gives accurate, cited answers. Here is how it works in plain English, real ROI numbers, and how to get started.