When I first heard about prompt engineering,
I thought:
it's a scam.
How can "Explaining something in natural language(i.e. English)" be engineered?
Even it is, it must be over-engineering
However, I was wrong.
By changing(engineering) the prompt, you can get:
- More accurate output
- More succinct output
- Less noise, and exactly what you want
With LLM's, you want to hit the bullseye, and not the sides, as much as possible, to reduce the hallucinations.
Here are 2 prompt engineering techniques:
1. Few-Shot Prompting
Give examples of the format you want.
The model learns from the examples in the prompt:
This is an example input:
Input: London,
Output: LON.
Input: Stockholm,
Output: ARN.
Input: Copenhagen
Output: ?
And AI would output this:
CPH
2. Chain-of-Thought(CoT)
Chain-of-thought is about forcing reasoning steps before the final answer.
Without the chain-of-thought(CoT), you could create a prompt like this:
A shop sells a laptop for $1000.
There is a 20% discount,
then 10% tax is applied.
What is the final price?
You might get the right answer.
You might not.
The model may shortcut or miscalculate.
Using Chain-of-thought, you would write this query instead:
Think step by step.
First calculate the discounted price.
Then apply tax.
Then give the final answer.
Now the model does something like:
20% of 1000 = 200
Discounted price = 800
10% tax on 800 = 80
Final price = 880
Answer: $880
These are just 2 out of many techiques of prompt engineering.
By getting the habit of creating deliberate prompts, you can get better results from AI.
Considering how much AI entered our daily lives, I think learning prompt engineering is huge!
