better prompting with pseudocode by eric elliott

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certainly! eric elliott's concept of "better prompting" can be understood as a technique to enhance interaction with ai models, ensuring they understand the user's intent more accurately. this approach emphasizes clarity, context, and structure in the prompts provided to the ai.

better prompting with pseudocode

basic principles of better prompting

1. **clarity**: the prompt should be clear and unambiguous.
2. **context**: provide sufficient background information for the ai to understand the task.
3. **structure**: use a structured format to guide the ai's response.

pseudocode for better prompting

here's a pseudocode template that illustrates how to construct better prompts:

```plaintext
function createbetterprompt(task, context, examples):
prompt = "task: " + task + "\n"
prompt += "context: " + context + "\n"

if examples are not empty then
prompt += "examples: " + examples + "\n"

return prompt
end function

// usage
task = "generate a list of fruits"
context = "the list should include common fruits found in grocery stores."
examples = "1. apple\n2. banana\n3. orange"

finalprompt = createbetterprompt(task, context, examples)
print finalprompt
```

breakdown of the pseudocode

1. **function definition**: the `createbetterprompt` function takes three parameters:
`task`: the specific request you want the ai to perform.
`context`: background information to help the ai understand the task better.
`examples`: optional examples that illustrate what you're looking for.

2. **constructing the prompt**:
the function constructs a `prompt` string that includes the task, context, and any examples provided.

3. **conditionally add examples**: if examples are provided, they are appended to the prompt.

4. **return and print**: the final prompt is returned and can be printed or sent to the ai for a response.

code example

now, let’s implement this in a simple javascript code snippet that simulates how you might use ...

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