This "memory" is known as state management and is the key to truly helpful and human-like interactions. Without effective state management, every sentence would be a new conversation, making meaningful conversation impossible.
Why state management is so important
Imagine you ask a chatbot: "What is the capital of France?" It answers, "Paris." Your next question is: "How many inhabitants does it have?" Without state management, the bot wouldn't know that the "they" refers to Paris. It might be confused or give an irrelevant answer.
State management solves this problem by storing the context of the entire conversation. It allows the bot to:
- Reference: understanding pronouns or implied topics.
- Follow-up questions: Answering questions that build on previous answers.
- Interruptions: Resuming a process (e.g. a booking) after the user has been briefly distracted.
- Personalization: The use of user information (name, preferences) throughout the entire session.
The three central methods of state management
Chatbots use various techniques to store and manage the context of the conversation:
Slot Filling
This is a common approach for targeted conversations, such as bookings or form filling. The bot identifies "slots" (or variables) that are necessary for a specific task (e.g. {destination}, {date}, {number of people}).
- Procedure: If a user says: "I would like to book a flight to London", the bot fills the {destination} slot with "London". If information is still missing, the bot asks specifically for the empty slots.
Advantage: Very structured and efficient for clearly defined tasks.
Context Windows (context window)
Modern chatbots, especially those based on Large Language Models (LLMs), use a context window. This is a buffer that stores the last user inputs and bot outputs.
- Procedure: The bot not only sends the user's current question to the AI model, but also the entire content of the context window. The LLM can thus interpret and answer the current query in light of the most recent history.
Advantage: Flexible and ideal for open, fluid conversations. The limitation is the maximum token length of the model.
Session & user state (session and user state)
In addition to the direct content of the conversation, bots often store more comprehensive information:
- Session State: Stores the current status of the interaction process (e.g. "The user is currently in booking step 3"). This data is discarded at the end of the session.
- User state: Stores persistent information about the user, such as preferred language, previous orders or the name. This data is retained between sessions.
- Advantage: Enables processes to be continued and greater personalization.
The technical challenge
State management is technically demanding. The challenges are:
- Storage: Where is the data stored? (Databases, in-memory storage such as Redis, or directly in the front end of the application).
- Expiry: How long should the context be stored? (A few minutes of inactivity ends the session to save storage space and ensure data protection).
- Scaling: How is it ensured that the context history of the right user is retrieved when millions of users interact simultaneously?
Well-implemented state management is at the heart of any advanced chatbot. It transforms a simple interaction machine into an intelligent, patient and context-sensitive conversation partner. For companies, it is the decisive factor that distinguishes a frustrating bot from a real value-added tool.