Let’s consider:
- Growing evidence of negative effects on learning
- Biases
- Hype
- Valid anti-AI sentiment
- Issues with training material (piracy, fair use considerations, copyright)
- Provider intentions
🚫 Hype 🚫
(hint: disregard the hype and use what makes you more efficient)
“If you don’t become an AI/prompt engineer by tomorrow you’re a loser with no future”
“Your product needs AI or it will be worthless”
“All your training in data and coding was a waste of time”
Still…
I believe everyone deserves the ability to automate tedious tasks in their lives with computers - Simon Willison
..providing a way for people to talk with machines in plain language constitutes a dramatic step forward in making computing accessible to everyone - Carl Bergstrom (paraphrased)
Where are the models? Who makes them?
Many popular LLMs are hosted by cloud providers
Many more! (let’s have a look)
Cloud-Hosted models
- Relatively easy setup
- Create account, set up billing if applicable, get an API key
- Access to new and in-development models and massive computing power
Local models
Download smaller models on our own hardware
- Data not shared with a provider
- No ongoing costs
- Work offline
- Steep learning curve
- Large memory requirements
- Slower performance
Examples:
Ollama (Simplifies running open-source LLMs locally)
Hugging Face transformers (lots of open-source models)
Which model do I use?
Considerations
- Pricing
- Free tiers, price per token, billing policies
- Speed
- Privacy
- Hardware
On pricing
Nothing is more costly than something given free of charge
- Japanese proverb.
nothing is offered for free unless there is something to be gained for the party involved
if we are not paying for the product, then we are the product
Interacting with LLMs
In the browser
Web-based chat interfaces, often with conversation histories and file upload features.
Programatically through an API
Application Programming Interfaces (APIs) are the rules and protocols that determine how one application can request data from another.
APIs
Let different software systems talk to each other and exchange data without having to understand each others’ inner workings.
APIs
Allow your applications (R scripts, browser-based prompts, mobile apps) to:
- Send input text (prompts)
- Specify which LLM model to use
- Receive generated text, code, or structured data back
- Handle authentication (e.g., using API keys)
API Keys
Working with APIs means we need to identify ourselves to the model provider and authenticate our encrypted connection to their servers
Overall workflow
- Create and account with a model provider
- Generate an API key
- Keep it safe but reachable by your LLM tools
Why LLMs + R
![]()
AI-generated image created with DALL·E 3 via Bing Image Generator
Decent enough outputs
- Working code, actual problems solved
Explosion of programs and features in late 2024
- Relevant keynotes, blog posts, demos
Reduce context switching
Shifting our attention between different tasks or programs can be tiring and make us less productive and efficient.
Browser ↩︎️↪️ IDE ↩︎️↪️ Word Processor ↩︎️↪️ Cellphone
Lots of copying and pasting may introduce errors.
‘Continue’ extension
“an open-source AI code assistant”
- runs as an extension in VS Code (and OSS Code forks including Positron) and JetBrains
Cool IDE features:
- Chat
- Autocomplete
- Context Items
ellmer and friends
![]()
Bridging R and LLMs
Robust and designed specifically for easy interaction with LLMs directly from R
Broad provider support and meant for entreprise/production use
Allows models to extend their capabilities by executing R code
Can produce output that is immediately usable in R
ellmer demos
chatGroqDemo.R
extractPDFdemo.R
gander
- More than a simple chat window
- Powered by
ellmer
gander receives a snapshot of our environment with every request and recognizes what we are talking about, including object and variable names
ganderDemo.R
Not part of this talk but interesting
(and somewhat covered in the Quarto Guide)
Model Context Protocol (MCP): the “USB-C port for AI applications”
Resource Augmented Generation (RAG): enhance the accuracy and reliability of LLMs with additional information from relevant data sources.
AI agents: Leverage MCP to do many things in with some degree of ‘autonomy’
Staying Up-to-Date
- Very dynamic and fast-moving field
- Key players are constantly innovating and collaborating
- Conferences often introduce novel tools
- Blogs and social media
Closing thoughts
- LLM-based tools are not always necessary. The answers you need may already be in the documentation, a book, your colleagues, a blog or tutorial, etc.
- Use for efficiency but always verify outputs
- Experiment and share your successes!
Acknowledgements
Team at posit making, maintaining, and improving several of these tools: Simon Couch, Garrick Aden-Buie, Hadley Wickham, Joe Cheng, Tomasz Kalinowski, Winston Chang, and many others.
MLverse team.
Albert Rapp and Chris Brown for sharing tutorials early in the life cycles of many of these tools.
Other developers and Maintainers: Frank Hull, Gabriel Kaiser
Everyone that shares, gives me feedback on the guide, or points me to need tools I missed.
