LLMs

Description

The LLMs panel lets you choose which large‑language model powers each mentor. mentorAI is model‑agnostic, so every tutor can run on the LLM that best fits its purpose—OpenAI GPT‑4 for nuanced writing help, Gemini for advanced reasoning, or even a custom model you integrate yourself.

Target Audience

Instructor

Features

Per‑Mentor Flexibility

Assign different LLMs to different mentors, tailoring performance, cost, and capabilities to each use case.

Two Quick Access Paths

Open the provider list either by clicking the model name on the mentor card or by selecting the LLM tab from the mentor dropdown.

One‑Click Switching

Pick a provider, choose a model, and see an immediate Success confirmation.

Provider‑Agnostic Platform

Supports OpenAI, Google, and other vendors—plus your own custom integrations.

Extensible Model Library

Add new or proprietary LLMs at any time; they appear alongside built‑in options for seamless selection.

How to Use (step by step)

Open LLM Settings (Method 1)

On the mentor card, click the text showing the current LLM name
The provider list opens

Open LLM Settings (Method 2)

Click the mentor’s name to open its dropdown
Select the LLM tab—arrives at the same provider list

Pick a Provider & Model

Click a provider (e.g., OpenAI or Google)
Select the desired model from the list
A Success message confirms the switch

Repeat as Needed

You can switch providers or models anytime
Each change shows a success confirmation

Add a Custom LLM (Optional)

If your preferred model isn’t listed, integrate it via the platform’s custom LLM interface
Once added, it appears with the built‑in providers and can be selected the same way

Pedagogical Use Cases

Domain‑Specific Tutors

Connect a healthcare mentor to a medically fine‑tuned model while keeping a literature mentor on a more creative LLM.

Cost Management

Run high‑traffic, low‑stakes mentors on a budget‑friendly model and reserve premium models for advanced courses.

Experimental Research

Quickly swap models to compare answer quality, reasoning depth, or speed—useful for instructional design studies.

Language‑Focused Mentors

Choose a multilingual model for language courses, ensuring better translation and pronunciation guidance.

Compliance & Privacy

Integrate an on‑premise or proprietary LLM for sensitive data scenarios, keeping information within institutional boundaries.

With simple, per‑mentor switching and support for custom models, the LLMs feature ensures each tutor runs on the engine that best meets its educational goals.