How to Summarize Your Entire Chat with Any LLM
How to Summarize Your Entire Chat with Any LLM
When working with large language models (LLMs), conversations can quickly become long and complex. Summarizing the entire chat into a clear, structured format helps you see the flow of dialogue at a glance, making it easier to analyze, document, or share.
One effective method is to transform the conversation into a two-column table:
- User: Simplified version of your request or message
- LLM: Simplified version of the model’s response
This approach distills the essence of each exchange without losing the overall narrative.
Why Summarize Chats?
- Clarity: Long conversations are condensed into digestible snapshots.
- Analysis: You can track how questions evolve and how the model responds.
- Documentation: Useful for teaching, auditing, or creating workflow guides.
- Sharing: A table format is easy to present in reports or articles.
The Instruction to Use
To get an LLM to produce this summary, you need a precise instruction. Here’s a template:
Take the entire conversation between me and you.
Summarize each turn into simplified text.
Output the result as a Markdown table with two columns: "User" and "LLM".
In the "User" column, write my simplified requests.
In the "LLM" column, write your simplified responses.
Example Output
| User | LLM |
|---|---|
| What is B | Explanation of B |
| Write this as an article | Article draft with headings |
The Intent Behind This Instruction
The goal is not just to shorten the chat, but to capture its rhythm and meaning. By reducing each turn to a short phrase, you create a high-level map of the conversation. This makes it easier to revisit past discussions, identify patterns, and repurpose content for documentation or training.