How Can I Deal with Long Texts When Using a Large Language Model?

Using large language models (LLMs) like GPT can be very helpful for many tasks. But sometimes, the texts we want to analyze are too long. Long texts can be a challenge because most AI models have limits on how much they can process at once. This article will explain how to handle and make the most of long texts when working with AI.

Why Do Long Texts Cause Problems?

Most AI models have a maximum token limit. A token can be a word, part of a word, or punctuation. For example, GPT-3.5 has a limit of about 4,096 tokens, and newer models can process more. When the input exceeds this limit, the model cannot read the entire text at once. This causes issues because some parts of the text might be cut off, leading to incomplete answers or missed details.

Break Down the Text into Smaller Parts

One of the simplest solutions is to split the long text into smaller sections. Divide the document into paragraphs or sections that fit within the model’s token limit. Feeding these parts individually allows the AI to process and analyze each segment separately.

Afterward, combine the insights gained from each section. This method works well for summaries or extracting key points from large documents. Remember to keep logical boundaries in mind so the AI can understand the context within each part.

Use Summarization Techniques

Summarization helps reduce long texts into shorter versions that highlight the main ideas. Tools or AI features that generate summaries can shorten lengthy documents to essential points. These summaries fit within token limits and still provide useful information.

You can start by asking the AI to summarize sections of your text. Once summarized, you can analyze or process this shorter version rather than the entire original text.

Incorporate Context with Sequential Prompts

When splitting texts, maintain context by referencing earlier parts in subsequent prompts. For example, after summarizing a section, you can ask the AI to remember key points when working on the next part. This approach helps keep the information coherent and connected across multiple segments.

Using techniques like chaining prompts, where each prompt builds on previous answers, can improve the quality of analysis even for multi-part texts.

Use Specialized Tools for Long-Text Handling

Some tools are designed to handle long documents more efficiently. These include document processing software that can automatically split texts or maintain the entire context. Integrating such tools with AI platforms allows for better management of lengthy texts.

Another option is to use models specifically designed for longer inputs. Some newer AI models can process tens of thousands of tokens, reducing the need to split texts as often.

Focus on Relevant Sections

If only certain parts of a long text are needed, focus on those sections directly. Skip irrelevant parts to keep the input shorter. For example, if you're analyzing a report to find conclusions, you don’t need every detail in the middle.

Selecting relevant parts ensures the model’s input stays manageable and makes its output more accurate about the specific information you want.

Clarify Your Goals

Be clear about what you want from the AI. Are you looking for a summary? Specific facts? A list of key points? Knowing your goal helps you decide how much text to include and what parts to emphasize. This clarity reduces unnecessary processing and helps the model give better answers.

Test and Adjust

Handling long texts with AI often requires trial and error. Test your approach with smaller samples. Adjust how you split the text or what parts to include. Over time, you'll find the best way to work with long documents in your specific tasks.

Final Tips

• Use concise prompts to get focused answers.
• Avoid overloading prompts with unnecessary details.
• Use multiple shorter prompts instead of one very long one.
• Keep the structure clear and logical for better results.

Handling long texts with AI takes planning and practice. By breaking down texts, summarizing, maintaining context, and focusing on relevant sections, you can get useful results without being limited by token caps. These methods help make your interactions with large language models more effective and efficient.