You have an Excel spreadsheet with 10,000 rows. You need to multiply two columns, row by row.
Calculate everything by hand.

Good luck!

Write =A1*B1 and drag the formula down.

You are done before the coffee is ready.

Ask your favourite AI to calculate every result.

Sorry, you have exhausted your quota. It will reset in 6 days and 9 hours.

Answer D.

Is that your final answer?

The formula: cheaper, more reliable, faster. A win all round.

You have a list of 1 million messages to sort into five categories. The first 5,000 have already been classified.
Classify everything by hand.

I didn’t come here to suffer, okay?

Train a small model on the 5,000 examples.

A classifier. What if that were enough?

A classifier.An agent with twelve toolsand three subagents!A classifier.
Ask your favourite AI to classify each message.

I’m financially comfortable.

Ask Jev to do it.

Fast, cheap and easy to set up.

JevClassifier

Is an LLM always the right tool for your problem?

A bazooka to kill a fly

An LLM can discuss the role of calves in German literature, give you a strawberry tart recipe, hack Australian health insurance or count the r’s in “strawberry”.

Do you really need to know how to translate Ancient Greek into Armenian to program in Rust?

Your mission, should you choose to accept it: classify emails using a single tool of your choice. The LLM is one of the options. With the same budget, the best tool for this task will make, compared with the LLM:
More errors.

You can choose the LLM as your tool.

It is one of the options, so the best available tool cannot perform worse than it.

The same number of errors or fewer.

The LLM is one of the options.

The LLM could be the best possible tool. If so, use it! Otherwise, use the right tool.

Definitely fewer errors.

The specialised tool does not necessarily do better.

An LLM may be the best solution. If so, use it. Otherwise, use a specialised tool.

Stockfish plays chess, AlphaGo plays Go. We do not ask them to write a cover letter.

If you have a specific task in mind, does your tool need to know how to do the endless other possible tasks?

A prototype is not a product

Twenty clients ask you to send follow-up emails. What do you do?
Create 20 agents and add the follow-up to their prompts.

Twenty prompts to write. Twenty behaviours to make reliable.

If the follow-up rule changes, you will need to update and verify it in each of the twenty agents.

Write a different automation for each client.

Twenty clients, twenty chances to fix the same bug.

This may address real differences, but it duplicates the shared mechanisms.

Build a shared follow-up feature, configurable for each client.

The need repeats. The code can be reused.

Separate the shared mechanism from the settings: delay, recipients and content.

With an LLM, the time from a blank page to a sexy demo is unbeatable. The bill will come later, when you need to make it reliable and keep developing it.

The prototype helps you discover the recurring pattern: wait, check whether a reply has arrived, then follow up if needed. This mechanism can become a product feature. Differences between clients become configuration.

LLMs take you from 0 to 1. Software engineering takes you from 1 to n.This article has not been published yet.From prototype to product: lessons from track and traceThis article has not been published yet.