Vectorless RAG: How I Built a RAG System Without Embeddings, Databases, or Vector Similarity
A journey from “vector similarity ≠ relevance” to building a reasoning-based RAG system that actually understands documents Photo by Becca Tapert on Unsplash Introduction Retrieval-Augmented Generation (RAG) has become a foundational pattern for building AI systems that can answer questions over private data. Traditionally, RAG relies on vector embeddings to retrieve relevant chunks of text, which are then passed to a language model for generation. However, as systems scale and use cases become more complex, a new paradigm is emerging: Vectorless RAG , also known as reasoning-based retrieval . Instead of relying on embeddings and similarity search, vectorless RAG navigates information like a human would — following structure, reasoning step-by-step, and dynamically deciding where to look n
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Cortex Code in Snowflake: How to Use It Without Burning Credits
Snowflake Cortex Code (CoCo) is like an AI assistant inside Snowsight (and CLI also). You can ask it to write SQL, create dbt models, explore data, help in ML work, and even do some admin tasks. But one thing people don’t realise early — this tool is powerful, but also costly if used wrongly. Bad prompts → more tokens → more credits → surprise bill. Prompt Engineering (this directly impacts cost) CoCo works on token consumption. what you type → counted 2. what it replies → counted If your prompt is vague → more tool calls → more cost. Example: Bad: Help me with my data Good: Create staging model for RAW.SALES.ORDERS with not_null on ORDER_ID Best Practices: Use full table names 2. Be clear about output 3. Keep prompts small 4. Provide business logic upfront 5. Use AGENTS.md for consistency
Best Form Backend for Job Applications and Event Registrations in 2026
If you're collecting job applications or event registrations online, you've probably hit the same wall. Either you're overpaying for a tool like Typeform or JotForm, or you're cobbling together a Google Form that looks unprofessional and gives you zero control over where your data goes. In this article, I'll walk through the best form backends for job applications and event registrations in 2026, covering price, features, file upload support, and which one is right for your use case. Why the Right Form Backend Matters for Applications and Registrations A contact form getting 10 submissions a month is simple. A job application form getting 500 submissions a month is a different problem entirely. You need: File uploads: Candidates submit resumes, cover letters, and portfolios. High submissio
The Stack Nobody Recommended
The most common question I got after publishing Part 1 was some variation of "why did you pick X instead of Y?" So this post is about that. Every major technology choice, what I actually considered, where I was right, and where I got lucky. I'll be upfront: some of these were informed decisions. Some were "I already know this tool, and I need to move fast." Both are valid, but they lead to different trade-offs down the line. The Backend: FastAPI I come from JavaScript and TypeScript. Years of React on the frontend, Express and Fastify on the backend. When I decided this project would be Python, because that's where the AI/ML ecosystem lives, I needed something that didn't feel foreign. FastAPI clicked immediately. The async/await model, the decorator-based routing, and type hints that actu
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O que uma usina nuclear tem a ver com o seu processo de QA?
A gente sabe que testar e validar um software antes de ir para produção é importante. Mas você já parou para pensar no peso real que isso carrega? Recentemente, estava revendo a série Chernobyl , e ela me fez refletir sobre muita coisa — especialmente sobre a forma como encaro minha área, sendo QA, e sobre a responsabilidade que ela traz. Resolvi compartilhar isso com vocês. Para quem não conhece, Chernobyl é uma minissérie dramática lançada em 2019 que retrata o desastre nuclear ocorrido na usina de mesmo nome, na então União Soviética, em 26 de abril de 1986. A história acompanha os eventos logo após a explosão do reator número 4 — o caos, as tentativas do governo soviético de esconder a gravidade do acidente e o enorme esforço de cientistas, bombeiros, militares e trabalhadores que arri
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