1. Intro: When Vector Store Choice Actually Matters “You can’t fix retrieval latency with better prompts.” If you’ve ever tried scaling an RAG (Retrieval-Augmented Generation) pipeline beyond toy use cases, you already know this: the choice of your vector store can make or break the entire setup. I’ve gone through that pain myself. Personally, I … Read more
1. Introduction “You can’t fix a broken hiring pipeline with a prettier job board.” I’ve seen it firsthand: companies drowning in thousands of resumes, relying on brittle keyword filters and outdated screening heuristics. In my own projects, especially when working with early-stage startups or talent platforms, I kept running into the same bottleneck—resume review. It’s … Read more
1. Introduction: Why GraphRAG Over Traditional RAG “If all you have is a vector store, every problem starts to look like semantic search.” That was me about six months ago—staring at yet another LLM hallucination, even though I had meticulously embedded the right documents. The truth is, traditional RAG starts to break the moment your … Read more
1. Intro: Why Prompt Chaining Isn’t Just a Hack — It’s an Architecture Pattern “Treat your prompts like APIs, not notes to a genie.”That’s something I learned the hard way. When I first started integrating LLMs into real-world pipelines, I used to think a well-engineered prompt could carry the whole workload. Write a good prompt, … Read more
1. Intro: Real-World Context “The problem isn’t storing vectors. The problem is doing it fast, cheap, and securely inside AWS.” I’ve had to pick a vector database for AWS more than once, and honestly, it’s never as simple as just comparing benchmark scores or reading spec sheets. In one project, I was building a real-time … Read more
1. Introduction: Why I Built This and What You’ll Get “A tool is only as good as the friction it removes.” I didn’t set out to build yet another RAG demo. I built this because I was tired of the disconnect between flashy LLM prototypes and what it actually takes to ship something usable. You’ve … Read more
1. Introduction: Why AI Needs a New Kind of Database “You don’t bring a knife to a gunfight — and you definitely don’t bring a relational database to an AI problem.” I learned this the hard way. Back when I started experimenting with large-scale semantic search, I initially tried to jam embeddings into Postgres with … Read more
Introduction: What This Guide Covers — and What It Doesn’t “Ship fast, iterate faster — but only if it works in production.” If you’re like me, you’re probably tired of tutorials that show you how to play with toy datasets, throw a model behind an API, and call it an “AI app.” That’s not what … Read more
I. Introduction “If you’re not streaming your LLM outputs, you’re probably leaving UX on the table.” That’s something I learned the hard way while building a real-time agent dashboard for internal use. When I first hooked Langflow into a Node.js backend, I assumed the API would behave like any other — simple request-response. But the … Read more
1. Introduction “If you’re not building local, you’re depending on the cloud to think for you.”That thought hit me when I was iterating on a retrieval-augmented generation (RAG) system for a client project—and latency was killing us. I’ve found that relying on external APIs for embeddings or vector storage is fine… until it’s not. Whether … Read more
