When I first started interacting with large language models (LLMs) like those powering Claude and Gemini, I was struck by how seamlessly they maintained context in conversations. It felt as if the model remembered our entire exchange, allowing for a natural back-and-forth dialogue. However, this perception is an illusion—one that masks a fascinating mechanism behind the scenes.

The reality is quite different from what the user interface suggests. LLMs don’t inherently “remember” past turns of a conversation. Instead, they rely on a clever technique involving prompt construction. Every time you send a message, the entire conversation history is concatenated and formatted into a single prompt. This giant prompt is what’s actually sent to the LLM for processing.

The User Interface Illusion

When you chat with Claude, you see a conversation unfolding turn by turn. You type a message, the model responds, and you continue the exchange. This gives the illusion of a continuous dialogue where the model remembers what was said before. In reality, each turn is processed independently, but the LLM maintains context through prompt construction.

The Reality: Prompt Construction

Let’s break down how this works with an example:

  • User: What’s the capital of France?
  • Claude: Paris.
  • User: And what’s its population?

Here’s what happens behind the scenes:

  • User asks: “What’s the capital of France?” -> This is converted into a prompt: "What's the capital of France?" This prompt is sent to the LLM.
  • Claude responds: “Paris.” -> The LLM generates “Paris.” This is displayed to you.
  • User asks: “And what’s its population?” -> Before this question is sent to the LLM, the entire conversation is combined into a new prompt: "What's the capital of France? Paris. And what's its population?" This entire string is sent to the LLM.

The LLM receives this long prompt, including your initial question, the model’s previous response, and your current question. It then generates an answer based on this complete context. It doesn’t “remember” that it said “Paris” before; it simply sees it in the prompt.

Why is This Done?

This approach allows the LLM to maintain context without needing internal memory. By providing the full conversation history, the LLM can understand the current question in light of what was discussed earlier. It’s a simple yet effective way to ensure that each response is relevant and coherent.

Analogy: Conversing with Someone Who Has a Terrible Memory

Imagine you’re talking to someone who has a terrible memory. Every time you ask them a question, you have to remind them of everything you’ve talked about so far. You’d say something like, “Remember we were talking about animals, and I asked you about dogs, and you said they bark. Now, what about cats?” The LLM works similarly. The prompt is like you summarizing the entire conversation for the LLM before asking the next question.

Practical Implications for Knowledge Workers

For knowledge workers, understanding how LLMs handle conversations has significant practical implications. Here are some key points to consider:

Limit Long Conversations

Given that each turn in a conversation involves sending the entire history as a prompt, long conversations can become inefficient. The longer the conversation, the more data the LLM needs to process, which can lead to increased latency and potential loss of context due to token limits.

Tip: Keep conversations concise and focused on specific topics. If you find yourself needing to discuss multiple unrelated subjects, it might be better to start fresh chats for each topic.

Use Summarization Techniques

To mitigate the issues of long conversations, you can ask the LLM to summarize the key points before starting a new chat. This way, you retain the essential information without overwhelming the model with excessive context.

Example:

  • User: Can you summarize our conversation so far?
  • Claude: Sure! Here’s a summary of our discussion: We talked about the capital of France and its population. We also discussed the Eiffel Tower and its history.
  • User: Great, thanks. Now let’s talk about something else.

By summarizing, you can effectively “reset” the conversation while keeping important information at hand. This approach helps maintain efficiency and clarity in your interactions with LLMs.

Learning Journey: Personal Insights and Challenges

In my experience, grasping how LLMs handle conversations has been both enlightening and challenging. Initially, I was baffled by how these models could seem so conversational despite lacking internal memory. It took some experimentation and research to understand the role of prompt construction.

One of the challenges I faced was managing long conversations. I often found myself losing track of the context or experiencing delays due to the length of the prompts. However, once I started using summarization techniques and breaking down discussions into shorter, focused chats, my interactions became much more efficient.

Key Takeaways

Understanding how LLMs handle conversations is crucial for maximizing their effectiveness in knowledge work:

  • LLMs maintain context through prompt construction, where each turn includes the entire conversation history.
  • Long conversations can be inefficient due to increased latency and token limits.
  • Use summarization techniques to retain essential information and start fresh chats when needed.
  • By applying these insights, we can enhance our productivity and make the most of LLM-powered tools in our daily tasks. Whether you’re conducting research, drafting reports, or brainstorming ideas, a clear understanding of how LLMs work will undoubtedly improve your workflow.
  • If long conversations are necessary, Gemini is the clear winner, with a context length of up to 2 million tokens with Gemini 1.5 Pro. No other service even comes close.