Fetching Context with Large Language Models

In this lesson, we will explore how Large Language Models (LLMs) can fetch context from various sources to enhance their responses. Context is crucial in interactions with LLMs as it allows the model to provide more accurate and relevant answers. Providing context manually might be a time-consuming process. By understanding how to provide context effectively, you can improve the quality of the responses you receive from LLMs and learn to work with them faster.

Let's examine how LLMs can summarize lesson material to provide a concise overview. This is particularly useful when you want the LLM to make better judgments in subsequent requests.

Imagine you have a piece of lesson material that you want the LLM to summarize. You can start by providing the material and asking for a summary:

Please summarize the following lesson material:

<your lesson>

The LLM will then generate a summary based on the provided material. This helps the LLM understand the key points, which can be useful for future interactions. You can follow-up the summarization with the following:

• Ask LLM to improve the lesson based on some feedback,
• Ask LLM to generate PDF handout with lesson summary to give to students,
• Ask LLM to come up with practices for students.

Next, let's explore how LLMs can fetch context from uploaded files, such as PDFs. This is useful when you have documents that you want the LLM to interpret or explain.

Imagine you have a PDF with an assignment, and you want the LLM to explain its format. You can upload the PDF and ask the LLM to provide an explanation:

Start with clicking the attachment icon in the input field:

Once the file is attached, you will see it appear in the input area. Now you can write your prompt:

The LLM will analyze the PDF and provide an explanation of its format, such as the structure of the questions or the layout of the document. Here is an example of a summarized document:

While LLMs are powerful tools for fetching context, they do have limitations, especially when dealing with complex files. For example, if you provide a PDF with a music score and ask the LLM to explain it, the LLM may struggle to provide a detailed explanation due to the complexity and specialized nature of the content.

You can leverage LLMs to fetch context directly from materials, enhancing efficiency by reducing the need for manual explanations. Here are some examples:

• Image Analysis: Instead of describing the content of an image to the LLM, you can upload the image directly. The LLM can then analyze and understand the image content, providing descriptions or insights much faster than manual input.

• Document Interpretation: When dealing with complex documents, such as research papers or historical texts, you can upload the files directly to the LLM. This allows the LLM to extract and understand the context without requiring a detailed manual explanation of the document's content.

• Data Set Analysis: For subjects involving data analysis, you can upload data sets directly to the LLM. This enables the LLM to understand the data context and provide insights or visualizations without needing a detailed description of the data structure.

• Textbook Content Summarization: Instead of explaining the key points of a textbook chapter, you can input the chapter text into the LLM. The LLM can then summarize the content, providing a quick overview that can be used for lesson planning or student handouts.

These examples demonstrate how submitting materials directly to LLMs allows them to fetch context more efficiently, reducing the need for manual explanations and speeding up your teaching process.

In this lesson, we explored how LLMs can fetch context from various sources, including lesson materials and uploaded files. We also discussed the limitations of LLMs when dealing with complex information. By understanding these capabilities and limitations, you can effectively utilize LLMs for context-driven interactions.

As you move on to the practice exercises, remember to experiment with different types of content to see how LLMs handle context. This will help you gain a deeper understanding of their practical applications and improve your ability to craft effective prompts.