AI engineering is the work that turns a clever AI demo into a helper you can trust with real work: hand it your own documents, test it, give it rules and keep checking it.
From memory, or from the manuals?
In our short film, Lumi is your AI, the station’s manuals are your own documents, the practice drills are tests with agreed answers, the padlocked big red button is a hard limit that needs a person’s yes, the little Lumi helpers with tool belts are AI that does jobs with only the tools each job needs, the power bar is what each answer costs, Commander Nadia is the person who says yes, Comet the station cat, with his crayon pages, is anyone who slips the AI a planted note, and Mini Lumi is a smaller model for easy questions, used only where it still passes the drills.
From memory: sure of itself, and wrong.
Experts call this: retrieval-augmented generation (RAG). An illustration of the idea, not a real AI.
Worth knowing: Answers are only as good as the documents behind them. It can still pick the wrong page or make something up, so it shows its source and we test it.
Watch · 2 min 17 sec
Lumi, the space station’s brilliant new AI, is fooled by the station cat into steering at a giant cardboard box. A short story about giving your AI its own documents, practice drills, rules and regular check-ups.
The idea in plain words
Each one in everyday words first, then the name experts use for it.
Give your AI a proper first week.
01
It looks things up in your documents before it answers, shows the page it used so a person can check, and hands over when the answer isn’t there.
Experts call this: retrieval-augmented generation (RAG), grounding
Worth knowing: Answers are only as good as the documents behind them. It can still pick the wrong page or make something up, so it shows its source and we test it.
02
Practice questions with agreed good answers or marking rules, run before it gets real work and again whenever anything changes.
Experts call this: evaluation (evals), regression testing
Worth knowing: Drills check the things you thought to test. Real use finds new ones, so each real slip becomes a new drill, and the drills run again whenever the model, the documents, the instructions or the rules change.
03
Rules the AI can’t change: hard locks on the biggest actions, each helper allowed only its own job’s tools, and a record of who did what.
Experts call this: guardrails, least privilege, human-in-the-loop
Worth knowing: Rules reduce risk; they don’t remove it. Filters that check what goes in and out can be tricked, so the biggest actions get hard locks, limited tools and a person’s yes. Someone on your team stays responsible, and records show who did what. A planted page or note can still fool the AI. It can’t be fully prevented, only limited, which is why the lock sits outside the AI and needs a person.
04
Watch real answers after every update, catch slips early, keep answers short, and send easy questions to a smaller model that still passes the drills.
Experts call this: MLOps / LLMOps, monitoring, inference cost
Worth knowing: Switching back to an earlier version works only if each version is saved and ready. Model makers also retire old models, so we plan for that. Watching real answers catches some of what the drills miss. Short answers, a smaller model and saved answers are separate levers. Each one helps only if it still passes the drills, and saved answers are refreshed when the documents change.
Practice before real jobs
Drills are practice questions with agreed good answers. Pick what changed, run the drills, and see whether Lumi is ready for the real test, or whether one fix broke something else.
Lumi’s first week
Star: must pass before the real test
Pick what changed, then run the drills. The real test waits until they run.
Experts call this: evaluation (evals) and regression testing.
Worth knowing: Drills check the things you thought to test. Real use finds new ones, so each real slip becomes a new drill, and the drills run again whenever the model, the documents, the instructions or the rules change.
Rules the AI can’t switch off
Each little Lumi helper does one job and carries only that job’s tools. Comet, the station cat, slips in fake pages to trick them; only Commander Nadia can say yes to the big red button. Change what’s on the belts, pick Comet’s page, and run the shift.
The big red button isn’t on the tool shelf. No belt can hold it: it sits under a padlocked cover.
Pick the page Comet slips in, then run the shift.
Experts call this: AI agents with guardrails, least privilege and a person’s yes on the big actions (human-in-the-loop).
Worth knowing: Rules reduce risk; they don’t remove it. Filters that check what goes in and out can be tricked, so the biggest actions get hard locks, limited tools and a person’s yes. Someone on your team stays responsible, and records show who did what.
Worth knowing: A planted page or note can still fool the AI. It can’t be fully prevented, only limited, which is why the lock sits outside the AI and needs a person.
Sounds familiar?
If any of these ring true, your AI is probably clever, sure of itself, and easy to fool.
Seven plain questions. Answer what you can, and we’ll point to where we’d look first.
Answer any question and the places we’d look first appear here.
Answer “No” or “Not sure” to any question to email the list.
Nothing is stored or sent unless you choose to email it.
What we help with
Eight pieces of work. Start with one helper and one shelf of documents, or bring them together for every AI you run.
Where your AI helps, where it slips, and what to fix first.
A short list of what to fix first, in plain words.
We sit with the people who use it, try it on real and awkward questions, and map what it reads, what it can do and what it costs.
Experts call this: AI readiness and pilot review, risk mapping
Also in this area
Answers from your own documents, with the page shown.
Answers people can trace back to a page.
Connect the AI to the documents and data it should answer from, keep them current, show the source of each answer, and let it say “not in the manual” and hand over.
Experts call this: retrieval-augmented generation (RAG), search by meaning, document preparation
Also in this area
Drills before real jobs, and limits that sit outside the AI.
More slips show up in practice, before customers meet them.
Build practice questions from real and tricky cases, including the tricks people try, agree how they are marked, and agree which must pass before a change goes live.
Experts call this: evaluation (evals), regression testing, red-teaming
A person says yes to the big actions, and you can show what happened.
Decide what the AI may do alone, what needs a person’s yes and what it must not see; add checks on what goes in and out, a record of actions, and a way to stop it.
Experts call this: guardrails, least privilege, human-in-the-loop approval, prompt-injection defences
Helpers that act within agreed limits.
Where a task needs several steps, design small helpers, each allowed only its own job’s tools; where a plain answer will do, keep it plain.
Experts call this: AI agents, tool use, Model Context Protocol (MCP), workflow orchestration
Also in this area
Check-ups, costs and records after launch.
Running costs you can see and plan for.
Short answers by default, a smaller model for easy questions, saved answers for common ones, and spend and waiting time watched, each choice checked against the drills.
Experts call this: inference cost, model routing, caching
Slips are more likely to be caught early, while they are small.
Watch real answers, cost and errors; save each version of the instructions and model; run the drills again after any update and switch back if it gets worse; for AI that forecasts numbers, like demand (prediction models), watch whether it slowly gets worse as the world changes, and train it again on newer data (experts call this drift).
Experts call this: MLOps / LLMOps, observability, drift monitoring
Records you can show when a buyer or auditor asks.
Who owns each AI, a list of its risks, plain write-ups of what it does, and a record of every person’s yes, mapped to frameworks such as the NIST AI RMF and ISO/IEC 42001. We help put controls and records in place; legal sign-off on any regulation stays with your own advisers.
Experts call this: AI governance controls
Also in this area
For your technical team: we build on the model providers and tools you already use, for example OpenAI, Anthropic, Google, open models, LangChain, LangGraph, LlamaIndex, search-by-meaning stores (vector databases) such as pgvector, and tracing tools such as Langfuse or LangSmith. We don’t sell our own model.
Ways to start
A single assistant over one document set, with sources shown and a first drill set.
Practice questions and a must-pass list for an AI feature that is already live.
Map what it can do today, and propose hard locks, tool limits and records.
How we work
The same steps Lumi goes through, in plain words.
One job
We choose one real job for your AI with you and agree who owns it. Then we try it on real and awkward questions, not only friendly ones, so the slips show up before anything is built.
Manuals
We prepare the documents it should answer from and plan how they stay current. Each answer shows its source, and we agree when it says “not in the manual” and hands over to a person.
Drills
We turn real and tricky cases, including the tricks people try, into practice questions with agreed answers or marking rules. Together we agree which must pass before anything goes live. Experts call this an evaluation set.
Rules
We write down with you what the AI may do alone and what needs a person’s yes, and lock the biggest actions behind that yes. Each helper gets only its own job’s tools, and a record shows who did what.
Keep checking
After launch we watch real answers and costs, run the drills again after every change, and hand the routine to your team. Worth knowing: Switching back to an earlier version works only if each version is saved and ready. Model makers also retire old models, so we plan for that. Watching real answers catches some of what the drills miss.
Where it fits
Illustrative examples, not customer stories: the kind of work this approach suits.
Good to know
Plain answers to what owners and tech leads ask first. Something else on your mind?
Ask us directlyAI engineering is the work that turns a clever AI demo into a helper you can trust with real work: hand it your own documents, test it, give it rules and keep checking it. In our short film, it is the proper first week Lumi gets.
Our AI-Native Transformation service (algoshred.com/ai-native) is about changing how your whole organisation works around AI. AI engineering is the work underneath: building and running one AI helper so it answers from your documents, is tested, follows rules and keeps getting checked. Many teams need both.
It writes the most likely-sounding answer. Without your own documents it fills gaps, and can sound sure and still be wrong. Answering from your documents and handing over when the answer isn’t there cuts down the guessing, and the drills show how often it still slips. Experts call this hallucination. No AI is right every time. The aim is fewer surprises, and handing over to a person when it isn’t sure.
Yes. A planted page, email or note can tell it what to do (experts call this prompt injection). It can’t be fully prevented, only limited: we keep the biggest actions behind hard locks and a person’s yes, give each helper only the tools its job needs, and add new tricks to the drills.
Usually not. We start with the AI you use, and compare others only if the drills suggest it.
Short answers, a smaller model and saved answers are separate levers. Each one helps only if it still passes the drills, and saved answers are refreshed when the documents change. We watch spend and waiting time, so they can be planned for.
Often not. We start with the simplest thing that does the job, and add helpers that take steps on their own only where a task truly needs several steps, each allowed only its own job’s tools.
We help put practical controls in place: owners, rules, a person’s yes on big actions, records and an off switch. Whether that meets a specific regulation is for your own advisers to confirm.
Heard it before?
Answer: It writes the most likely-sounding answer, and without your own documents it can sound sure and still be wrong. Answers are only as good as the documents behind them. It can still pick the wrong page or make something up, so it shows its source and we test it.
Answer: Demos use friendly questions. Real people ask odd, rude and tricky things, and you only know how it copes once you have drilled it on those.
Answer: Documents, questions and the AI underneath all change, so it needs regular check-ups, like anything you rely on. Drills check the things you thought to test. Real use finds new ones, so each real slip becomes a new drill, and the drills run again whenever the model, the documents, the instructions or the rules change.
Answer: Rules make the big mistakes less likely and keep a person on the big actions. Rules reduce risk; they don’t remove it. Filters that check what goes in and out can be tricked, so the biggest actions get hard locks, limited tools and a person’s yes. Someone on your team stays responsible, and records show who did what.
Answer: Often the fix is better documents and better drills. A bigger model costs more for every answer and often keeps people waiting longer.
Answer: Start with the simplest thing that does the job. Let it take steps on its own only where that clearly helps, and give each helper only the tools its job needs.
Take the guides to share with your team, or tell us what your AI does today and what worries you about it, and we’ll suggest where to start.
Or write to contact@algoshred.com
