Using AI Assistants with searcher

Introduction

The searcher package now enables direct interaction with various AI assistants directly from your R environment. This vignette explains how to use these features to enhance your R programming workflow by leveraging AI-powered assistance.

Available AI Services

The package supports the following AI services:

ChatGPT (ask_chatgpt()) - OpenAI’s popular large language model
Claude (ask_claude()) - Anthropic’s assistant known for longer context and thoughtful responses
Perplexity (ask_perplexity()) - Research-focused AI with internet search capabilities
Mistral (ask_mistral()) - Mistral AI’s assistant with strong reasoning capabilities
Bing Copilot (ask_bing_copilot() or ask_copilot()) - Microsoft’s AI assistant with web search integration
Meta AI (ask_meta_ai()) - Meta’s conversational AI assistant

Basic Usage

To use any of these AI services, simply call the corresponding function with your query:

library(searcher)

# Ask ChatGPT a question about R
ask_chatgpt("How do I create a scatterplot with ggplot2?")

# Get Claude to explain a statistical concept
ask_claude("Explain GAMs (Generalized Additive Models) in R")

# Research time series forecasting methods
ask_perplexity("What are the best time series forecasting packages in R?")

# Debug a problematic R function
ask_mistral("Debug this function: calculate_median <- function(x) mean(x)")

# Compare programming approaches with Bing Copilot
ask_copilot("Compare data.table vs dplyr for large datasets")

# Ask Meta AI about best practices
ask_metaai("What's the best way to handle missing data in R?")

Customizing AI Behavior with Prompts

One powerful feature of the AI search functions is the ability to customize how the AI responds by using prompts. This can be done in two ways:

1. Per-call Prompts

For one-time customization, provide a prompt parameter to any AI search function:

# Ask for step-by-step debugging
ask_chatgpt("Why doesn't this work? mtcars %>% filter(cyl = 4)",
              prompt = "You are an R debugging expert. Identify the error and explain step by step:")

# Request tidyverse-focused solutions 
ask_claude("How to reshape data?",
             prompt = "Answer using tidyverse packages, particularly tidyr:")

# Ask for educational examples
ask_perplexity("How to implement PCA in R?",
                 prompt = "Provide a beginner-friendly tutorial with examples:")

2. Default Prompts via Options

For persistent customization, set default prompts in your .Rprofile or at the beginning of your session:

# Set default prompts
options(
  searcher.chatgpt_prompt = "As an R data visualization expert, please help with:",
  searcher.claude_prompt = "Provide reproducible R code examples for:",
  searcher.perplexity_prompt = "Provide evidence-based R solutions with references:"
)

# Now all queries will use these default prompts
ask_chatgpt("How to create a heatmap?") 
ask_claude("Efficient way to merge multiple dataframes")

Error Handling Integration

A particularly useful feature is the ability to automatically search your errors with AI assistants:

# Set Claude as your error handler
options(error = ask_claude)

# Now any error will automatically be sent to Claude
fibonacci <- function(n) {
  if(n <= 0) return(0)
  if(n == 1) return(1)
  return(fibonacci(n-1) + fibonacci(n-2))
}

# This will cause a stack overflow error that will be automatically searched
fibonacci(1000)

# You can also manually search the last error
ask_chatgpt()  # Searches the last error message

Effective Prompting Strategies

Well-crafted prompts are essential for getting the most out of your interactions with AI assistants. By providing clear, structured prompts, you can dramatically improve the quality, relevance, and usefulness of AI responses. The prompts can also be tailored to the specific AI service you are using, as each has its own strengths and weaknesses or “quirks.” This section explores effective prompt strategies for different R programming scenarios.

Understanding Prompts

Prompts serve as instructions that guide how an AI assistant should respond to your query. A good prompt typically includes:

Role specification: Who the AI should act as (e.g., “You are an R programming expert”)
Task definition: What the AI should do (e.g., “Debug this code”)
Output format: How responses should be structured (e.g., “Provide step-by-step explanations”)
Context: Any relevant background information (e.g., “This is for a beginner’s tutorial”)

Tailoring Prompts to Different AI Services

Different AI services have different strengths and characteristics. Varying your prompts or selecting the right service can yield better results. Here are some general guidelines for tailoring prompts to specific AI services:

ChatGPT: Responds well to direct, structured instructions and specific constraints
Claude: Excels with narrative prompts and nuanced instructions about reasoning
Perplexity: Works best with prompts that request citations and multiple perspectives
Mistral: Benefits from clear, concise prompts with explicit output formatting
Bing Copilot: Responds well to prompts that involve web knowledge and current information
Meta AI: Still a newcomer, but generally effective with straightforward queries

Debugging Code

When debugging R code, effective prompts should guide the AI to identify the specific issue and provide a clear explanation.

# Less effective prompt
ask_chatgpt(prompt = "Fix this code:")

# More effective prompt
ask_chatgpt(prompt = "You are an R debugging expert. First identify what's wrong with this code without fixing it. Then explain why it's wrong and what concept I'm misunderstanding. Finally, provide a working solution with an explanation of why it works.")

This improved prompt is effective because it:

Establishes a clear role (debugging expert)
Structures the analysis process (identify → explain → solve)
Focuses on learning (explaining the underlying concept)

Learning New Concepts

When using AI to learn R concepts, prompts should encourage clear explanations with progressive complexity.

# Less effective prompt
ask_chatgpt(prompt = "Explain this R concept:")

# More effective prompt
ask_chatgpt(prompt = "As an R educator teaching a diverse classroom, explain this concept in multiple ways: 1) Start with an intuitive explanation a beginner would understand, 2) Provide a simple working example, 3) Explain how this concept connects to other R concepts like {relevant_concepts}, 4) Show a more advanced practical application with commented code.")

This approach works well because it:

Creates a teaching scenario that encourages clear communication
Requests multiple perspectives (from basic to advanced)
Asks for concrete examples, not just theory
Encourages connections to existing knowledge

Selecting Packages and Tools

When seeking advice on package selection, prompts should encourage comprehensive, balanced comparisons.

# Less effective prompt
ask_perplexity(prompt = "What package should I use for this?")

# More effective prompt
ask_perplexity(prompt = "As an unbiased R consultant familiar with the entire CRAN ecosystem, compare the top 3-4 R packages for this task. For each package, discuss: 1) Key strengths and limitations, 2) Ease of use and learning curve, 3) Community support and maintenance status, 4) Performance characteristics, 5) Unique features. Conclude with situational recommendations (when each would be the best choice) rather than a single recommendation. Include citations to benchmarks or articles where relevant.")

This strategy works because it:

Explicitly requests multiple options instead of a single answer
Defines specific comparison criteria
Asks for situation-dependent recommendations
Requests evidence/citations to support claims

Code Review and Improvement

For code review prompts, focus on balancing constructive criticism with actionable improvements.

# Less effective prompt
ask_mistral(prompt = "Review this code:")

# More effective prompt
ask_mistral(prompt = "As a senior R developer conducting a code review: 1) Note what the code does correctly, 2) Identify potential issues in correctness, performance, readability, and maintainability, 3) Suggest specific improvements with before/after code examples, 4) If relevant, mention R idioms or functions that would simplify the code, 5) Rate the code on a 1-10 scale for efficiency, readability, and robustness.")

This approach is effective because it:

Balances positive feedback with constructive criticism
Covers multiple dimensions of code quality
Provides specific examples, not just general advice
Includes a structured evaluation framework

Complex Statistical Analysis

When seeking help with statistical methods in R, prompts should emphasize both theoretical understanding and practical implementation.

# Less effective prompt
ask_claude(prompt = "Help me analyze this data:")

# More effective prompt
ask_claude(prompt = "As both a statistician and R programmer, help me with this analysis task. First, explain the appropriate statistical approach and why it's suitable for this situation. Then, provide an R implementation with explanations of: 1) Required packages, 2) Data preparation steps, 3) The analysis code with comments explaining each step, 4) How to interpret the outputs, 5) Diagnostic checks to validate assumptions, 6) Potential limitations of this approach. Show output examples where helpful.")

This works well because it:

Connects statistical theory with R implementation
Provides a complete workflow from preparation to interpretation
Includes validation and limitations
Balances code with explanations

Creating Visualizations

For data visualization queries, prompts should focus on design principles, not just code implementation. Though, you do want to specify if the visualization should be created with a specific package (e.g., ggplot2, plotly, etc.).

# Less effective prompt
ask_chatgpt(prompt = "Create a visualization of this data:")

# More effective prompt
ask_chatgpt(prompt = "As a data visualization expert who specializes in R: 1) Recommend 2-3 visualization types that would best represent this data and explain why, 2) For the most appropriate visualization, provide ggplot2 code with a clear aesthetic mapping rationale, 3) Suggest specific customizations to improve readability and visual appeal, 4) Explain how the visualization could be modified to highlight different aspects of the data. Follow ggplot2 best practices and modern data visualization principles.")

This approach is effective because it:

Focuses on visualization strategy, not just implementation
Explains the reasoning behind design choices
Provides customization options
Grounds recommendations in best practices

Experimenting with Different Approaches

The power of prompt engineering comes from experimentation. Consider how these two prompts would produce different results for the same query about handling missing data:

# Technical focus
ask_claude(prompt = 
  "As an R package developer with deep knowledge of data structures, explain all approaches to handling missing values in R, including their algorithmic implementations, performance characteristics, and edge cases."
)

# Applied focus
ask_claude(prompt = 
  "As a data scientist who regularly cleans messy datasets, share your practical workflow for handling missing values in R. Include code examples using both base R and tidyverse approaches, focusing on real-world scenarios and decision criteria for when to use each technique."
)

Both are well-structured prompts, but they would yield different responses focused on either technical depth or practical application.

Advanced Prompt Management

While the prompt examples in this vignette provide useful templates, the searcher package offers a more powerful and flexible prompt management system. This system allows you to:

Maintain a library of specialized prompts for different tasks
Set system-wide prompts that apply across all AI services
Layer multiple prompts for precise control
Save and reuse your most effective prompts

For comprehensive documentation on these advanced features, see the dedicated vignette:

vignette("managing-ai-prompts", package = "searcher")

With the prompt management system, you can move beyond single-use prompts to create a personalized library of AI instructions tailored to your specific R workflows and projects.

Conclusion

The AI assistant integration in searcher provides a way to access AI help directly from your R environment without needing an API key or external setup. By customizing prompts, you can tailor the AI’s responses to your specific needs, making it an even more powerful tool for R programming, data analysis, and problem-solving. Though, keep in mind that AI services have different strengths or “quirks”, so experiment with each to find which works best for your particular needs.

James Joseph Balamuta

2025-05-20