Checklist
Beyond Market Intelligence keeps Checklist in one place: 4 stories so far. The section currently leads with “Production RAG's Missing Piece: Confident Answers, Honest Refusals”, “Why Reproducibility Must Be More Than a Checklist Promise”, and “How AI agents detect phishing by noticing what humans overlook”. Moving a RAG prototype to production means confronting the gap between demos and dependable answers. Four papers for AAAI 2027, and not a single line of code or data to inspect. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Checklist story on Beyond Market Intelligence, newest first.
Production RAG's Missing Piece: Confident Answers, Honest Refusals
Moving a RAG prototype to production means confronting the gap between demos and dependable answers. The checklist we needed focuses on citations for every response, the confidence to refuse when context is insufficient, and access control that resists clever workarounds. Ingestion should never block a user. This is about building trust through honesty. For a deeper look at how systems handle uncertain inputs, our guide on multi-armed bandit simulations offers a useful parallel. Watch the full breakdown here.
Why Reproducibility Must Be More Than a Checklist Promise
Four papers for AAAI 2027, and not a single line of code or data to inspect. That is a frustrating start to any review cycle. The checklist exists precisely to prevent this gap between promise and proof. When a paper's core argument rests entirely on numbers you cannot verify, your confidence in those results should drop, and it is right to flag it. Asking for anonymized code during rebuttal is a fair, practical move.
How AI agents detect phishing by noticing what humans overlook
A single phishing email can now mimic a colleague's tone, timing, and context well enough to fool a careful eye. AegisAI, founded by former Google security executives, just raised $36 million to fight that exact threat. Their approach is refreshingly human: AI agents that read each message for subtle anomalies, the kind a checklist would miss. It's a practical, grounded defense for a growing problem.
Design Smarter Prompts by Letting AI Build Its Own Workflow
Prompts are the quiet force behind every useful interaction with a large language model, yet they often fail when consistency matters most. Meta-prompting changes that by asking the model to build its own reusable prompt, template, or workflow before diving into the task. It's a smarter approach for teams that need the same output format again and again. If you're tired of vague results, this technique deserves your attention.