Designing Multi-Turn Conversations: An AI That Keeps Track

One of the biggest challenges in prompt engineering and AI application development is creating a natural flow. Large language models (LLMs) are inherently stateless: they forget everything as soon as an API call is completed. It is up to us as developers to build the illusion of a continuous conversation. How do you ensure a multi-turn architecture that is robust, cost-effective, and user-friendly?

1. State Management: The Foundation of Your Architecture

To give a model memory, the backend must track the conversation history (the state) and send it back with each new iteration. For web applications, this usually means storing it in a database or in the memory of the active session.

The danger? The model's context window is limited (and expensive tokens pile up quickly). A naive approach simply sends the entire array of previous messages. A better strategy is the Sliding Window technique, where you, for example, only send the last 5 to 10 turns via your API endpoints, plus an optional fixed system prompt that defines the persona and basic rules.

2. Referencing Previous Turns in Practice

When a user says: "Make that header red instead of blue", the system needs to know which code they are referring to. Suppose you are building a web application where you call models like DeepSeek or Claude via a framework (such as OpenClaw). If the user refines a component, the context must contain the current code.

Example:
User (Turn 1): "Write a Python script for database backup."
AI (Turn 1): [Generates script]
User (Turn 2): "Add logging to the exceptions."

If the model does not have the previous Python script in its context at Turn 2, it will fail or generate only loose log lines. Ensure that the payload to the LLM remains structured with clear role: user and role: assistant objects.

3. Summarization: Surviving Token Limits

What do you do with conversations of 50+ turns? Constantly sending the entire history eats up your compute and budget. The solution is asynchronous summarization.

When the history reaches a certain token limit (e.g., 4000 tokens), you have a smaller, cheaper model write a summary of the oldest turns in the background. The new context payload will then look like this:

[
  {"role": "system", "content": "You are an expert..."},
  {"role": "system", "content": "Summary of previous context: The user is building a frontend app and has just completed the authentication module."},
  {"role": "user", "content": "Let's tackle the CSS now."},
]

For enterprise applications or complex systems, you can store these summaries in a vector database. If a user asks: "What did you say yesterday about those API keys?", you retrieve the specific turn via embeddings. Read more about implementing RAG and vector search here.

4. When to Start Over? (Context Reset)

Even the best summarization suffers from "context drift"—the model gets confused by too much diverse information in a single thread. It is crucial to facilitate a hard reset in both the UI and the backend.

For the user: Provide a prominent "New topic" or "Clear memory" button.
For the backend: Build in an intent classifier. If the system detects that the user suddenly switches from 'debugging Python' to 'a recipe for pancakes', the application can automatically split the state or start a clean context with the message: "I see we are starting a new topic. Shall I start with a clean slate?"