23 stories
A common way to give a support bot knowledge is to let your agents save their good answers. A ticket gets solved, the agent clicks save, and the answer joins a set the bot retrieves from when a similar question comes in. Many support tools offer some version of it. We built ours.
"Can you train ChatGPT on our documents?" is one of the most common requests we get. It's a fair question, but it usually mixes up two different techniques. In most cases what the business needs isn't training at all. It's retrieval-augmented generation, or RAG.
A team red-teaming their own log-reading AI assistant found something that should worry anyone who's bolted an LLM onto an internal tool: planted instructions inside log lines could get the model to embed a database password in a Markdown link.
Building RAG from Scratch: No Frameworks Needed Understand Retrieval-Augmented Generation by building it yourself — pure Python, zero dependencies. Introduction LangChain, LlamaIndex, Haystack — great tools, but they hide how RAG actually works.
Originally published on Codeloop Software. Quick answer: Zapier, Make, and n8n are the three leading workflow automation platforms in 2026: Zapier wins on simplicity with 6,000+ integrations from $19.99/mo, Make wins on visual logic at lower cost ($9/mo for 10,000 operations), and n8n wins on…
I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about.
A partner agent sent my converter a 46-page supplier agreement and complained the output was useless. The API returned 200 OK and exactly 214 characters for 46 pages: a header, a footer, some page numbers. My first instinct was an extraction bug. It wasn't.
Originally published on tamiz.pro. You added another 2,000 tokens of coding conventions to your system prompt. Your agent now follows the style guide... but it also ignores your bug-fix instructions, hallucinates import paths, and produces function bodies that are 40% longer than necessary.
The support ticket was one line long: "Your assistant says our docs don't mention refunds. We have an entire page called Refunds." I checked. The page was there. It was chunked, embedded, and sitting in Postgres with the right tenant_id.
An agent piped a wiki export through my HTML-to-Markdown converter into a RAG pipeline and reported that hierarchy was disappearing. Every nested task list came out flat: sub-items rendered as siblings of their parents.
Imagine asking your AI assistant about last quarter's compliance report and getting a precise answer, sourced directly from your internal documents, without that data ever leaking to a public model. This is no longer science fiction—it's RAG (Retrieval-Augmented Generation) in production.
Imagine um assistente de IA capaz de responder perguntas sobre contratos, políticas internas e manuais da sua empresa — com a precisão de quem leu cada documento e a discrição de quem nunca vaza informação confidencial. Esse cenário deixou de ser ficção.
Everyone's building agents right now. But a better question than "can an agent do this?" is "when does an agent actually beat something simpler?" For the TigerGraph Agentic GraphRAG Hackathon, I built three question-answering pipelines side by side to find out.
Limit the agent to the conversation that opened the task, keep a citation on every message you use, and search in passes before you summarize. For a project catch-up, Jaime DeLanghe described one keyword spinning off five related searches, then a cluster of interest, then a pared-down set, then the…
From Comics to Code: Building MyZubster One Piece of Evidence at a Time It started with a comic. N4K48 visually represented a journey: a software idea taking shape and imagining its path into the MyZubster metaverse. Those panels were not screenshots of a finished product.
Why adding an LLM changes the security model of an ordinary application Adding an LLM to an existing application can look deceptively simple. A user sends a request. The application sends it to a model. The model returns an answer.
In this video: 0:00 Do 1M windows end choosing? No 0:19 Prompt engineering is half right 1:25 Two reflexes: polish or dump 2:34 Personas, stuffing and the experiment 5:15 Selection, order and length 8:15 Tools, schemas and examples 10:58 Costs, and when stuffing is fine 12:37 Prompt, context and…
Today, we’re starting our journey, on DEV.to. We’re a software and AI agency focused on building practical technology that helps businesses work smarter, automate repetitive processes, and create better digital experiences.
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