Filtered by: AI & Agents × clear
Ten quick questions on building retrieval-augmented systems that scale under load and don't leak data they shouldn't — access control, prompt injection, PII, evals, and caching. No reading required, just test yourself.
My chat bot confidently lied about my own writing. The bug wasn't retrieval - it was an ambiguous sentence in the source and a stale index. Why RAG is a data problem before it's a retrieval problem.
Explainability has a fancy name, but it's built from things you already use - weighted scores, plain-English reasons, and an append-only audit trail. Here's a whole loan-approval workflow, walked through by persona.
I built a remote MCP server for my blog, then realized a bearer token was all I needed. Here's the honest trigger for when an MCP server is actually worth it, and how the build and auth really work.
The first agent I worked on had evals, but they only ran in CI/CD: a pre-flight checklist with no in-flight instruments. Here is the case for live, continuous evals as part of your observability: what to measure, what a decent score looks like, and how to tune them over time.
The right AI tool depends on who's asking, and every tool only works if it's grounded. How I split agentic tooling by persona for a fictional bank: DaaS golden paths for customers, a knowledge-graph-grounded analytics assistant for analysts, and a shared RAG knowledge base.
A walkthrough of the RAG-powered chat widget on this site - how it indexes blog posts, how grounding actually works, and why you don't need expensive managed services to build something solid.
At Google Cloud Next '26, a conversation about genBI made me rethink nearly 20 years of BI assumptions. Here are three shifts worth considering.
