A deep dive into AI-powered document processing The origin story ZapDoc is an AI-powered document processing platform that extracts structured data from PDF documents and delivers results in Excel format via email. I didn’t start out wanting to build a document automation platform. What I wanted was leverage. I’d seen firsthand how time-consuming and painful it was to pull structured data out of messy PDFs: invoices, CVs, contracts, proposals… all needing to be manually parsed, copy-pasted, or hand-entered into Excel.

o3 Plays Pokemon - The Journey

Have you ever felt like there’s a strange trade-off in the people you date — the kinder they are, the less attractive they seem? Or maybe doctors notice that patients either have diabetes or high blood pressure, but rarely both? That’s Berkson’s Paradox in action. It’s a statistical illusion that happens when we only look at a filtered or biased sample.

Use Hugging Face's smolagents framework to automate customer support for a fashion store Introduction Fashion retailers receive hundreds of customer emails every day. Some asking about products, others trying to place orders. Manually handling these messages is time-consuming, error-prone, and doesn’t scale. In this project, we tackle this problem by building an AI system that reads emails, classifies their intent, and automatically generates appropriate responses.

Most query expansion methods either dig through feedback from initial search results or rely on pre-defined thesauruses. Query2doc skips both. Instead, it uses LLMs to generate short, relevant pseudo-documents and appends them to your query — no retraining, no architecture changes. How It Works Use few-shot prompting (4 examples) to generate a passage based on a query. Combine the original query and the LLM-generated text:

And what to do about it Retrieval-Augmented Generation (RAG) was supposed to fix everything. More accurate answers. Less hallucination. Instant access to domain-specific knowledge. But in real-world deployments, the results often disappoint. Answers feel off. Retrievals are irrelevant. Context seems lost. So what’s going wrong? Let’s break down the core problems, and how to fix them. 1. Vector embeddings aren’t magic…