LLM-based AI for IR: Approaches and Strategies

As Institutional Researchers, we navigate an overwhelming landscape of AI technologies. Since late 2022, Large Language Models (LLMs) have transformed how we can approach data analysis, but choosing the right integration strategy can be challenging. In this article, I'll share a practical framework for understanding and implementing LLMs in Institutional Research based on my experiences as an IR analyst.

Three Approaches to LLM Integration

Through exploration and experimentation, I've identified three main approaches to integrating LLMs into IR workflows:

1. Chatbot Interfaces

The most accessible entry point is using web-based chatbot interfaces like ChatGPT, Claude, Gemini, or DeepSeek. These tools have rapidly evolved with new capabilities:

Enhanced reasoning for solving complex problems with step-by-step approaches
Web connectivity that extends beyond pre-trained datasets
Customization options with specific instructions for different roles
Multimodal processing capabilities for images, audio, and file uploads

While these interfaces are user-friendly, selecting the right model can be challenging. I recommend exploring leaderboards that benchmark different LLMs' performance on specific tasks (easily found by searching "leaderboards for LLMs"). Understanding how LLMs are trained helps explain why they perform differently for various tasks:

Pre-training on diverse datasets influences what content the model understands best.
Fine-tuning optimizes performance for specific tasks like coding or writing.
In-context learning depends on carefully crafted prompts that guide model responses.

The free versions of these tools offer immediate access with minimal setup, making them ideal for quick tasks and brainstorming. However, they have usage limits and lack advanced features. Premium subscriptions provide enhanced capabilities but lock you into specific ecosystems with recurring costs.

2. Code-Based Implementations

For those comfortable with coding, this approach offers exceptional flexibility with two sub-options:

API Integration

Connecting to LLMs through programming interfaces provides:

Cost efficiency with premium features at reduced costs (often just a few cents per project)
Model flexibility to switch between different LLMs based on task requirements
Access to advanced libraries like DSpy for optimizing prompts and LangChain for database connections

To get started with this approach:

Use Visual Studio Code (VS Code) with helpful extensions.
Familiarize yourself with Python packages.
Set up API keys on your chosen LLM website.
Study API quickstart guides for practical code examples.

Local Deployment

Running open-source LLMs on your own machine offers:

Maximum data security for handling sensitive information
Independence from external services
One-time setup rather than recurring costs

However, this approach requires:

Programming knowledge for implementation
Powerful hardware with good GPU resources for reasonable speed
Acceptance of potential performance trade-offs compared to cloud-based models

Tools like Ollama and LM Studio make local deployment more accessible, though you'll still need to understand basic programming concepts.

3. Integrated AI Applications

The third approach leverages AI capabilities built into software we already use:

Microsoft's Copilot in Excel and other Office applications
Google's NotebookLM as a reading companion
Various emerging AI tools and services

When evaluating these integrated solutions, consider:

Whether they come from established providers or emerging startups
Their pricing structures and available resources
Security and privacy statements, especially important for protecting student data

This approach represents the future direction of the industry, with more development expected in AI agents that provide end-to-end analysis.

Practical Examples from IR Work

These approaches enable various applications in institutional research:

Survey design using API calls to generate questions based on meeting notes and templates
Qualitative data analysis to extract themes and labels from student comments
Coding assistance for data cleaning and visualization scripts
Database integration for natural language querying of institutional data

Implementation Strategies

Based on my experience, I recommend the following:

Test different models to find optimal solutions for specific tasks.
Carefully evaluate outputs and refine prompts for better results.
Take small, manageable steps toward complex goals.
Continuously learn about emerging techniques through search engines, conferences, and community discussions.

Remember that learning these technologies follows a curve—be patient with yourself and celebrate small successes. Starting small makes the process less daunting, especially when building technical skills.

Ethical Considerations

As we implement LLMs, we must develop institutional standards for responsible use:

Data privacy protocols for FERPA-protected student information
Output monitoring to identify and address potential biases
Transparency in how AI tools are used in decision-making
Community engagement to develop shared standards collectively

Looking Forward

The future of IR will likely involve increased integration of AI capabilities into our workflows. While the current solutions might not yet fully meet their claims, IR professionals have an important role in guiding LLMs to produce reliable work that enhances rather than replaces human expertise.

Through understanding these three integration approaches—chatbots, code-based implementations, and integrated applications—we can better navigate the overwhelming landscape of LLM usage and select strategies that suit our institutional needs, budgets, and technical resources.

Linli-Zhou Linli Zhou is an IR Analyst at Lasell University who has pioneered LLM implementation for data analysis workflows. She has presented her innovative approaches at AIR Forum. She welcomes collaboration on LLM integration projects and occasionally shares her learning journey on YouTube (@LinliSharesResearch).