Amazon Bedrock supports multi-turn conversational applications by providing tools to manage context, maintain state, and leverage foundation models (FMs) designed for dialogue. Here’s how it works:
1. Context Management via API Bedrock’s API allows developers to pass the full conversation history (user inputs and model responses) with each request. Models like Claude or Llama 2 are trained to process this sequential data, using the history to understand context and generate relevant responses. For example, if a user asks, “What’s the weather in Tokyo?” followed by “Will it rain tomorrow?”, the model recognizes “tomorrow” refers to Tokyo. Developers must manage token limits by truncating or summarizing older interactions (e.g., retaining the last 10 messages) while preserving key details. Bedrock doesn’t store conversation state itself, so developers typically use session databases (e.g., DynamoDB) or caching services (e.g., ElastiCache) to track and retrieve history between API calls.
2. Customization with System Prompts Bedrock lets developers define a system prompt to set the chatbot’s behavior and role for the entire conversation. For instance, a prompt like “You are a travel assistant specializing in budget itineraries” ensures the model maintains this persona across all interactions. This is critical for consistency in multi-turn scenarios. Developers can also inject business-specific context (e.g., product catalogs) into prompts using Retrieval Augmented Generation (RAG) via Bedrock’s Knowledge Bases, allowing the model to reference external data during conversations without retraining.
3. Integration with Agents and Tools For complex workflows (e.g., booking flights), Bedrock Agents enable chatbots to chain multiple steps while retaining context. Agents can call APIs (via Lambda functions), query databases, or validate user inputs across turns. For example, a banking chatbot might authenticate a user in the first interaction, then reference that session token for subsequent balance inquiries. Bedrock’s trace functionality helps debug multi-step conversations by logging inputs, outputs, and tool usage. Pre-built integrations with services like Lex (voice/dialog management) and Connect (contact centers) further simplify building contextual experiences.
By combining these features, developers can build chatbots that handle follow-up questions, correct misunderstandings (“No, I meant Paris, France”), and complete multi-step tasks while maintaining a natural flow. The key implementation steps involve structuring API calls to include history, optimizing context windows, and using Bedrock’s tooling to handle stateful operations.