LibreChat Frequently Asked Questions (FAQs)

LibreChat

LibreChat Basics

What is LibreChat?

LibreChat is Bowdoin's internal generative AI platform that leverages the same technology behind ChatGPT and similar AI models. It offers a secure, chat-based environment for work and research, ensuring that your data and chat history remain private and protected within Bowdoin's internal infrastructure.

What makes LibreChat different from public generative AI chat tools such as ChatGPT and others?

When using public generative AI chat tools, data you enter is automatically shared with the company making that tool and may be used in a variety of different ways. LibreChat is an internal Bowdoin tool with agreements with model providers to not use Bowdoin data for model training, so the data entered into LibreChat is kept safe and secure while still allowing access to the same AI models that power popular tools like ChatGPT, Claude, and others.

Who has access to LibreChat?

LibreChat is available to all Bowdoin staff, faculty, and students. Staff and Faculty have access automatically while students must first complete the Learning Paths into GenAI course in Canvas before they will have access to LibreChat. (Staff and Faculty are also encouraged to take the course as well.)

Data Security and Privacy

What type of data is safe to be put into LibreChat?

Data security at Bowdoin is based on a three-level classification system. Currently, LibreChat is authorized for Level 1 and 2 data which includes public data and data that is private and should not be available to non-Bowdoin individuals without permission. More sensitive data that would fall under level three (such as data that must be kept confidential by contract or laws such as FERPA and HIPPA protected data) are not currently authorized for use in LibreChat. However, clearance to input higher levels of data is on the roadmap for future updates to the platform. For more information on these data classifications, you can refer to the Data Classification Guidance from Information Technology.

How secure is the data that I put into LibreChat?

Just like the data stored in Bowdoin systems like SharePoint, Microsoft Teams, college devices, etc., any data that is entered into LibreChat is kept within Bowdoin systems and is not used by any AI companies like Microsoft, OpenAI, or any other non-Bowdoin entity. The data, including prompts/questions and files, that you enter into LibreChat cannot be collected or used by any of these AI companies in order to train their models. Your conversation history is saved in your local device’s browser, and Bowdoin does not store your conversations unless you 1) share a conversation or 2) save your workspace.

General Features

What does temperature mean in LibreChat?

When using generative AI tools, temperature refers to how precise or how creative you want the output of the tool to be. Adjusting the temperature can help you tailor the output to meet your specific needs. You can select a temperature value between 0 and 1, and it affects how predictable or diverse the output will be.

Low Temperature (Closer to 0): Setting the temperature closer to 0 will produce more deterministic and repetitive responses. At a low temperature, the model is more likely to select the most probable words or phrases given the context, leading to text that is often more focused and precise but less varied and creative.
High Temperature (Closer to 1): As the temperature approaches 1, the model's responses become more stochastic or random. This means that less probable words or phrases have a higher chance of being selected, resulting in responses that are more diverse, creative, and less predictable.

When should I use the different temperatures?

Setting the temperature of a chat is primarily based on what your goal is with that conversation. If you're looking for precise, accurate information or responses, a lower temperature is generally more suitable. This might include technical explanations, specific instructions, or factual content. When your goal is to generate creative writing, brainstorming ideas, or you're looking for a variety of responses to explore different perspectives, a higher temperature value can help achieve that by introducing more novelty into the text. The temperature setting is a useful tool for experimentation and can serve as a way to adjust the model to more effectively adapt to your specific, unique use cases.

What AI models are available for use in LibreChat?

LibreChat provides access to a variety of Large Language Models. Currently, LibreChat users have access to the following models:

GPT-5
GPT-5 (Thinking)
GPT-5 (Latest)
GPT-5 (mini)
GPT-5 (nano)
o3-deep-research
o4-mini-deep-research
o3
o4-mini
GPT-4.1
GPT-4o
Claude Sonnet 4
Claude Opus 4.1
Amazon Nova Premier
Llama 4 Maverick
Llama 4 Scout
DeepSeek R1
OpenAIgpt-oss-120b

What does selecting a model mean, and how do I pick between models?

Selecting a model refers to choosing a specific large language model from one of the various providers, such as OpenAI, Anthropic, Mistral, etc. Each model has unique capabilities and strengths, and the choice of model can impact the content, tone, and style of the output you receive. For instance, one model might excel at technical writing, while another might be better suited for creative storytelling. By selecting a model, you’re determining which AI's expertise aligns best with your needs for a particular project or query. A useful strategy for determining what model is best for your purpose is to test each model with various prompts to identify the strengths and weaknesses of the responses.

You can select different models by clicking the model name in the top left of the main chat window.

How do I cite that I am using a large language model?

Policies around citing usage of generative AI tend to vary between disciplines. In general, both APA and MLA have guidance on how to cite usage of LLMs (APA citation guidelines or MLA citation guidelines). Additionally, different professional organizations and/or academic journals may have their own citation and disclosure policies. In general, it is good practice to cite and disclose all uses of generative AI while also being mindful of when use of generative AI is appropriate or not.

What file types can I upload to LibreChat?

Currently LibreChat works with most text-based files. While image and video files are not currently supported, the following file types are supported:

Comma-Separated Values (.csv)
Compressed file (.zip)
Excel spreadsheets (.xlsx)
Hypertext Markup Language - HTML (.html)
JavaScript (.js)
JSON format (.json)
Markdown (.md)
Plain text (.txt)
Portable Document Format (.pdf)
PowerPoint Presentation (.pptx)
Python format (.py)
Word documents (.docx)

Features of LibreChat

How can I manage files I’ve uploaded to LibreChat?

In the LibreChat chat bar, the Attach Files menu allows you to access all files you have uploaded to LibreChat in the past. This file manager allows users to access previously uploaded files without having to upload them for each new conversation.

Can I share and/or save conversations?

In LibreChat, you can share conversations with other LibreChat users. You can also download or export conversations so that you can save either a specific output or an entire conversation.

How does sharing work in LibreChat?

What is a Prompt Template (Prompts)?

A Prompt Template, or just Prompts, is designed to streamline the process of using the same prompt or sharing a prompt with slight differences. A prompt template is a prompt with placeholders. You can select a prompt template, fill in the placeholders and run the prompt with the model of their choice. This is a great tool if you create a detailed prompt that you plan to use to complete the same tasks but focused on different content.

What are Custom Instructions?

Custom Instructions in LibreChat are basically a set of rules at the start of a conversation designed to prime the AI tool to get a more focused and desired response. These instructions can include details about desired tone, style, or content, allowing you to customize the language model's responses to better suit their individual requirements. Examples of custom instructions could be ones focused on turning data into a visualization or taking detailed text and reformatting it into a PowerPoint presentation.

Custom Instructions are found under the Parameters menu in the right side-bar.

What is the difference between LibreChat Prompt Templates (Prompts), and Custom Instructions?

Custom Instructions are specific guidelines or commands provided by users to tailor the model's output to their specific needs. They are messages reinforced to the model throughout the conversation. Prompt Templates (or just Prompts) are pre-written prompts with placeholders. They can have specific custom instructions attached to them, further tailoring the conversation.

What is the limit on how much data I can input into a conversation?

The amount of data you can input into a conversation with LibreChat is subject to two main constraints: the character limit for text input and the maximum context window for each model. The character limit for text input is a fixed number that represents the maximum number of characters you can enter into the text box at one time. Currently, LibreChat has set this limit to 24,000 characters for all user inputs regardless of the model.

In addition to the text input limit, each model has a "maximum context window," which is the total amount of text (from both the user and the model's responses) that the model can consider when generating a response. This includes the history of the conversation. The size of the maximum context window varies between models and determines how many tokens (words or word pieces) the model can look back on at any given time.

Please note that the maximum context window is a separate constraint from the text input limit and is specific to each model's ability to process and remember conversation history.

What is a context window?

A context window in the realm of generative AI text tools refers to the amount of text the model can consider at any one time when predicting or generating the next word or sequence of text. It represents the "memory" of the model for the current task. When the model generates text, it looks at the words within this window to understand the context, maintain coherence, and ensure relevance to what has been said before.

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