Start here
A five-minute orientation, then a path in.
In short: AndyMental is one free, evidence-first drop a day on applied enterprise AI. This page tells you what it is, how to read it, and which of the six threads to start with.
Updated 19 October 2026
# What this is
AndyMental is applied enterprise-AI field notes by Anand “Andy” Padia — Global Head of GenAI at Trigent, shipping AI in production and writing down what actually holds. Every piece is evidence-first: a named model, paper, or incident with a date and a number, and one original claim about what it means for a working engineer or tech leader. No hype, no doom, no vendor copy. One drop a day.
# How to read it
This is a running field notebook, not a course — you don’t have to read it in order or catch up on everything. A few things worth knowing:
- One drop a day. Short field notes, occasional longer essays, TILs, and videos — each stands on its own.
- Evidence over opinion. Every claim carries a named case, or it’s labelled as judgement.
- Free forever. No paywall, no gate. Read anything; subscribe if you want it in your inbox.
# Choose a path
The drops are organised into six threads. Pick the one closest to what you’re working on — each opens a curated stream of every drop in that area.
# Ten guides to start with
If you want the long-form answers rather than the daily notes, these ten guides are the spine of the site. They read in order as a path from the first evaluation to a funded roadmap, and each stands alone.
- How to evaluate an LLM feature before production — Build the eval that can fail on the failures you cannot afford, before collecting easy cases.
- Why your RAG pipeline returns irrelevant results — Trace one wrong answer to the first stage that lost the passage, then fix that stage.
- Choose prompts, RAG, tools or fine-tuning — Match the fix to the observed failure; training is the last, evaluated option.
- How to choose an LLM for your actual workload — Eliminate every model that fails your task contract on your own cases, then rank on cost.
- How to keep multi-step AI agents under control — Host-enforced budgets, allowlisted tools, explicit stop reasons; verify the final state.
- What a production-ready LLM architecture needs — Eight components and a failure map with a named operator in every cell.
- How much does an LLM feature cost in production? — Price per accepted task; the escalation rate, not the token price, moves the bill.
- Should you build or buy AI agents? A decision method — One acceptance suite against every option; own the boundary where exit cost is decided.
- Build an AI roadmap around release decisions — Four gates with evidence, an owner and a stop rule; no invented dates.
- AI engineering resources for shipping your first feature — Pick each resource for the production artifact it helps you produce.
# Three ways in
- Read today’s drop — the newest one, front and centre.
- Browse the full archive — every drop, newest first, searchable.
- Skim all topicsif you’d rather see the threads side by side.
# Get one drop a day
If it’s useful, have it come to you. Subscribe for one drop a day by email — free, one-click unsubscribe, no spam. Or follow along on LinkedIn.