RAG

Obelisk is an AI-powered marketing platform that helps teams create content with brand consistency. It orchestrates specialized AI agents for SEO, email marketing, brand voice analysis, and strategy—all within a multi-tenant SaaS architecture with space-level isolation. Tech Stack: FastAPI, LangGraph, PostgreSQL, Vertex AI, Redis, Google Cloud Source: Private (commercial product) · Book a call to discuss The Hard Problems Building production AI systems exposes problems that don’t appear in tutorials: Agent reliability — LLMs hallucinate, get confused, and can be manipulated. How do you build agents that fail gracefully and resist prompt injection? ...

Every LangChain tutorial ends right where the real work begins. You see a neat 50-line script that queries a PDF, and you think, “Cool, I’ll ship this by Friday.” Three weeks later, you’re debugging memory leaks, wondering why your chain silently returns empty strings, and questioning every decision that led you here. I’ve shipped LangChain-based features to production at multiple companies. Here’s what I wish someone had told me before I started. ...

Introduction If you've ever found yourself digging through product manuals, company wikis, or lengthy documents just to find a simple answer, you know the pain. The fact you're reading this suggests you're interested in how Generative AI can make that process less painful. Stick around for a few minutes, and I'll walk you through how we built a smarter FAQ bot using Google's Gemini API, Retrieval Augmented Generation (RAG), and structured output. This isn't just another chatbot; it's designed to give reliable, context-aware answers based only on provided information, minimizing the risk of making things up (hallucination). This example uses Google Car manuals, but the principles apply anywhere you have a set of documents you need to query effectively. I'm sharing my journey building this; it's a practical demonstration, not a definitive guide, so adapt the ideas to your needs! The Problem: Dumb Bots and Information Overload Traditional search methods or basic chatbots often fall short when dealing with specific document sets: ...

Introduction If you've ever found yourself digging through product manuals, company wikis, or lengthy documents just to find a simple answer, you know the pain. The fact you're reading this suggests you're interested in how **Generative AI** can make that process less painful. Stick around for a few minutes, and I'll walk you through how we built a smarter FAQ bot using Google's Gemini API, Retrieval Augmented Generation (RAG), and structured output. This isn't just another chatbot; it's designed to give **reliable, context-aware answers** based *only* on provided information, minimizing the risk of making things up (hallucination). This example uses Google Car manuals, but the principles apply anywhere you have a set of documents you need to query effectively. I'm sharing my journey building this; it's a practical demonstration, not a definitive guide, so adapt the ideas to your needs! The Problem: Dumb Bots and Information Overload Traditional search methods or basic chatbots often fall short when dealing with specific document sets: ...