Management is all over the place, scrambling to ‘get AI’.
A common failure of that scramble is a reality: rubbish in — rubbish out.
Outcome quality depends on two factors:
- Data quality. The quality of the data that is used to generate that outcome. The quality, depth and breadth of the data is dictated by the databases on which the system was trained,
- Instructions given. The instructions you give the machine will drive the type and weight it gives to the available data in response to your instructions.
AI is a ‘machine’, an electronic warehouse of information it makes available on request.
They are machines, not people. They cannot ‘think, they do as instructed, using predetermined ‘training’ to prepare an answer.
Most people are radically unprepared for the changes coming.
The best known problem solving metaphor has always been Einstein’s.
He observed that if he had an hour to solve a life defining problem, the 1st 50 minutes would be spent defining the problem, the rest is just maths.
It is identical in the deployment of AI.
It seems to me that when a system ‘hallucinates’ it is a sign that it has been inadequately briefed. Think about the briefing as you would explaining something to any intelligent 10-year-old!
Keep it simple.
Explicitly define what information is to be used.
Explicitly define the objective to which the information will be directed.
Explicitly give any contextual information that may be helpful.
Explicitly define the range of outcomes you might be looking for.
The key to leveraging the speed and depth of data made available by AI systems is in the preparation of the data and the matching of that data to the problem being addressed.
If you use this simple process, the one you should have practised on your children, you can dodge the expensive and largely useless ‘prompt engineering’ courses, books, and gurus that have sprung up like mushrooms after rain. They are there to drain your pockets by offering seemingly easy solutions to difficult challenges.
There is no such thing as an easy solution that negates the necessity to ‘do the work’.
Header credit: DALL-E.
