Internal AI assistants are a low-friction way to apply large language models, answering questions like "How do I deploy this service?" The real challenge is retrieving scattered institutional knowledge from Confluence, Google Docs, and GitHub. Retrieval-Augmented Generation (RAG) bridges this gap, grounding LLMs in your specific documents for accurate, up-to-date answers.

For AWS users, Amazon Bedrock Knowledge Bases simplifies building a RAG without managing a vector database. You can connect data sources like Confluence, chunk content, and generate embeddings using Amazon Titan. This setup syncs with OpenSearch Serverless to create a searchable knowledge base, eliminating the need for constant fine-tuning as documentation evolves.

Next, you build a Slack bot using the Slack Bolt SDK. The bot parses messages, queries the Bedrock Knowledge Base, and invokes a foundation model like Claude to generate a response. Prompt engineering is key; instructing the model to answer only from retrieved context prevents hallucinations and keeps responses grounded in your internal data.