Conversation
LLMs can maintain context across multiple turns of conversation. Each message builds on previous ones, enabling follow-up questions and iterative refinement.
Under the hood, the full conversation history is sent with each request.
What are the three laws of thermodynamics?
The model provides an initial answer.
The three laws of thermodynamics are:
1. Energy cannot be created or destroyed, only
transformed (conservation of energy).
2. In any energy transfer, the total entropy of a
closed system always increases.
3. As temperature approaches absolute zero, the entropy
of a system approaches a minimum value.
Now we ask a follow-up. The model knows "the second one" refers to the second law from its previous answer.
Can you explain the second one with a simple analogy?
The model uses context from the conversation to give a relevant, connected answer.
Think of a hot cup of coffee in a cold room. The heat
always flows from the coffee to the room — never the
other way around. The coffee cools, the room warms
slightly, and the energy spreads out.
That "spreading out" is entropy increasing. You can
never un-spread it without adding external energy
(like a refrigerator, which uses electricity).
You can keep refining. Here we ask the model to adjust its previous answer.
Now explain it to a 5-year-old.
The model remembers the full context — it knows "it" refers to the second law — and simplifies further.
You know how when you drop a bit of paint in water, it
spreads out and makes the water colorful? It never
un-spreads back into a dot by itself.
Things in the world like to spread out and mix up.
That's just how nature works!
Key takeaway: Multi-turn conversation is powerful for exploration and refinement. Be aware that the entire history counts toward the model's context window — very long conversations may need to be summarized or trimmed.
Tips:
- Use follow-ups to drill deeper: "tell me more"
- Refine outputs: "make it shorter", "add examples"
- Reference earlier answers: "going back to point 2"
- For apps: manage conversation history to stay within
the model's context window limit