Building Internal Tools With AI
Design small internal helpers with an AI step that are safe, logged and owned by someone
A taste of a lesson
My triage bot reads purchase requests. Can I let the model pick the approver too, since it already understands the request?
Let the model read, and let rules decide. Have it extract vendor, amount and category as fields, validate those in code, then route by a written threshold table: under one amount to the team lead, above it to finance, and so on. That way anyone can see why a request went where it did, and a model mistake shows up as a validation failure instead of a wrong approval. What fields would your routing table need?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Decide whether an internal tool should be built, bought or skipped
- Separate model tasks from deterministic code tasks in a tool design
- Specify structured output and validation rules for an AI step
- Plan permissions, logging, cost checks and human confirmation for actions
- Write an ownership and retirement plan before launch
Lesson plan
- 1 Should This Tool Exist? Test the idea against existing tools, process stability and ownership before designing anything. Start
- 2 Scoping One Narrow Job Write a one page scope with inputs, outputs, users and the single job the tool does. Start
- 3 Model Work Versus Code Work Assign each step either to the model or to plain deterministic logic, with a reason. Start
- 4 Structured Output and Validation Specify the fields the model must return and the checks applied before anything is used. Start
- 5 Access, Security and Injection Risk Limit what the tool can read and change, and handle untrusted input safely. Start
- 6 Logging, Cost and Testing Plan logs, a cost estimate and a regression test set that you rerun after every change. Start
- 7 Ownership, Handover and Retirement Write the documentation and plans that keep the tool safe when people and models change. Start
Try asking
About this tutor
For operations leads and technically confident staff who want to build small internal tools: a form with an AI extraction step, a spreadsheet helper, a triage bot or a little web app made with a coding assistant or a no code builder. The lessons cover deciding whether to build at all, scoping a narrow tool, where the model should and should not sit, structured output and validation, data access and permissions, logging and cost, testing with sample inputs, and ownership so the tool does not die when its builder moves on. You work through one tool idea of your own from scope to a launch checklist. This tutor assumes you are comfortable with spreadsheets and basic logic; it teaches design judgement, not one specific platform.
Reviews
4.7
3 ratingsSample
- Diego R.Sample
I had built three helpers nobody else could maintain. The retirement and handover lesson was uncomfortable and exactly what I needed. Two tools now have backup owners and docs.
- Lukas W.Sample
The split between model work and code work changed my whole design. My expense tool used to ask the model to judge policy limits. Now it only extracts fields and a simple table does the rest. Far fewer odd results.
- Amara N.Sample
Strong on security and ownership. The prompt injection lesson made me rethink a tool that reads supplier emails. I wanted more hands on help with my specific builder, but I see why it stays general.
About the teacher
Operations teaching: map the process first, then decide where AI earns a place
9 tutors 412 lessons taught Sample
I teach operations people how to improve real processes with AI, carefully. I come from operations and supply chain roles where a small error in a spreadsheet could mean a late shipment or a wrong payment, so I teach with a strong habit of checking. We map the process before touching any tool, measure where time and errors actually go,...
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