gpt_prompter: Structured Prompt Builder for LLM (ChatGPT)

In laresbernardo/lares: Analytics & Machine Learning Sidekick

Structured Prompt Builder for LLM (ChatGPT)

Description

Build standard prompts to get the right outcomes using the four theoretical elements required to build standard and cleaner asks: instruction, input, context, and output. Inspired by the Prompt Engineering Guide free guide. Remember to start simple and be very specific to get exactly what you need.

Usage

gpt_prompter(
  instruction = NULL,
  input = NULL,
  context = NULL,
  output = NULL,
  quiet = TRUE,
  ...
)

Arguments

instruction, input, context, output	Character or vector. You do not need all the four elements for a prompt and the format depends on the task at hand.
quiet	Boolean. Should the written prompt be printed or not?
...	Additional parameters. You can pass cols parameter to explicitly set column names.

Value

(Invisible) list with written prompt and elements provided.

Elements of a Prompt

Instruction

a specific task or instruction you want the model to perform. It usually starts with a verb given its an instruction.

Input

input data, elements or question that we are interested to find a response for,

Context

external information, additional context or references to steer the model to better responses.

Output

type or format of the output.

See Also

Other ChatGPT: gpt_ask()

Other LLM: gemini_ask(), gpt_ask()

Examples

# A simple formatted table with data
# Note: I mostly use output = "table" and enabled an auxiliary enrichment prompt
(p <- gpt_prompter(instruction = "Capitals of the world", output = "table"))

# Classify
p <- gpt_prompter(
  instruction = "For each of the inputs, classify using only the options in context",
  input = c("Molecule", "Elephant", "Milky Way", "Cat", "Planet Earth"),
  context = c("Big", "Medium", "Small"),
  output = "table",
  # This cols parameter is auxiliary
  cols = c("Input", "Category"),
  quiet = FALSE
)

# Tag all categories that apply
p <- gpt_prompter(
  instruction = paste(
    "For each of the inputs, provide which of the",
    "context values apply as correct tags using TRUE/FALSE"
  ),
  input = c("I love chocolate", "I hate chocolate", "I like Coke", "Who am I?", "T-REX"),
  context = c("food", "positive", "negative", "beverage"),
  output = "table",
  quiet = FALSE
)

# Extract information from strings
p <- gpt_prompter(
  instruction = "For each of the inputs, extract each of the information asked in context",
  input = c("My mail is [e-mail]", "30 Main St, NY, USA", "[phone]", "$1.5M"),
  context = c("email", "full state name", "country of phone", "full non-abbreviated number"),
  output = "table",
  cols = c("Input", "Element_to_extract", "Value"),
  quiet = FALSE
)

# Translate to several languages
p <- gpt_prompter(
  instruction = "For each of the inputs, translate to the respective languages in context",
  input = rep("I love you with all my heart", 5),
  context = c("spanish", "chinese", "japanese", "russian", "german"),
  output = "table",
  cols = c("Input", "Language", "Translation"),
  quiet = FALSE
)

# Format date values
p <- gpt_prompter(
  instruction = paste(
    "For each of the inputs,",
    "standardize and format all values to the format in context"
  ),
  input = c("March 27th, 2021", "12-25-2023 3:45PM", "01.01.2000", "29 Feb 92"),
  context = "ISO Date getting rid of time stamps",
  output = "table",
  cols = c("Input", "Formatted"),
  quiet = FALSE
)

# Convert units
p <- gpt_prompter(
  instruction = paste(
    "For each of the inputs,",
    "provide new converted values using the units in context"
  ),
  input = c("50C", "300K", "100F", "0F", "32C", "0K"),
  context = "Fahrenheit",
  output = "table",
  cols = c("Input", "Original_Unit", "Total_Value", "Converted_Value", "New_Unit"),
  quiet = FALSE
)

# Read a text and answer a question related to it
gpt_prompter(
  instruction = "read",
  context = "Long text here",
  input = "Question here"
)$prompt

laresbernardo/lares documentation built on Oct. 23, 2024, 12:05 p.m.