Amazon Bedrock can be used to build a question-and-answer system for internal company documentation by leveraging its managed foundation models (FMs) and integration with data sources. The service provides a straightforward way to access large language models (LLMs) like Anthropic Claude or Amazon Titan, which can process natural language queries and generate accurate responses. By connecting these models to your internal knowledge base—such as documents stored in S3, wikis, or databases—Bedrock enables developers to create a system that retrieves and synthesizes information on demand without managing infrastructure.
To implement this, first prepare your data by converting internal documents into a format accessible to Bedrock. For example, you could use Amazon Textract to extract text from PDFs or images, then store the processed data in a vector database like OpenSearch or Amazon Aurora. Bedrock’s Retrieval Augmented Generation (RAG) capability can then link the LLM to this database. When a user asks a question, Bedrock searches the vector database for relevant snippets and feeds them to the LLM to generate a context-aware answer. For instance, if an employee asks, “How do I reset my VPN credentials?” the system retrieves the latest IT policy document and generates step-by-step instructions based on its content.
Security and customization are key considerations. Bedrock allows you to restrict model access to specific AWS IAM roles, ensuring only authorized users interact with the system. You can also fine-tune models using internal data (e.g., past support tickets) to improve accuracy for domain-specific terms. Additionally, parameters like temperature and top_p can be adjusted to balance creativity and factual correctness—critical for technical documentation. For example, setting a low temperature ensures the model prioritizes exact phrases from engineering manuals when answering questions about API endpoints. By combining Bedrock’s managed LLMs with existing AWS data tools, developers can create a secure, scalable Q&A system tailored to internal needs.