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The Truth About RAG

Retrieval-Augmented Generation (RAG) is a big leap for making AI agents genuinely useful in real-world settings. It brings together the best of large language models—like the ones powering ChatGPT—with your own data and documents. If you want your AI agents to stop hallucinating and actually know about your business, RAG is how you get there.

What is RAG?

Retrieval-Augmented Generation (RAG) is an approach that improves the accuracy and relevance of AI-generated responses by letting the language model “look things up” at the moment of generation. Instead of relying only on what was memorized during its initial training (often months or even years old), RAG lets the model search through external documents and databases to inject up-to-date, context-specific information into its answers.

Here’s how it typically works:

  1. You ask a question or send a prompt.
  2. The model searches a connected knowledge base (your docs, wikis, etc.) for the most relevant pieces of information.
  3. It combines the retrieved info with the original prompt.
  4. The AI then generates its answer, grounded in those recent or proprietary facts.

This dramatically reduces hallucinations, helps AI handle niche or private topics, and makes it possible to update the model’s “knowledge” instantly—no expensive retraining required.

How Does RAG Work in Runbear?

In Runbear, RAG is how you give AI agents real, live access to your team’s unique data. Our workflows are tailored for the kind of flexibility and control that real businesses need.

A typical RAG workflow on Runbear might look like:

  1. Connect Your Knowledge Source: Plug in your company’s Confluence, Notion, Google Drive, or upload key documents.
  2. Automatic Indexing: Runbear’s backend ingests and indexes this information as vector embeddings, making it searchable and ready for retrieval.
  3. Instant Queries: When someone in Slack, Teams, or another supported app asks a question, the agent retrieves the most relevant pieces of your content and adds them to the prompt sent to the LLM.
  4. Grounded Responses: The AI drafts its answer based on both your latest docs and its general language skills. No more “as of 2023…” dead ends!
  5. Citation-Ready: For use cases requiring traceability, Runbear RAG can cite sources, letting you cross-check the facts.

Use Cases

With RAG, there are many useful workflows that can be automated with Runbear.

Your legal team could use a compliance bot as the first line of defense. It could help provide customers with quicker and more accurate responses in time-critical jobs like healthcare. Financial analysts could use Runbear with business data to get insights. Or it could automate repetitive questions in your Slack.

What RAG Can Do

  • Keep AI Answers Up-to-Date: Instantly reflect new content, with no need to retrain.
  • Reduce Hallucinations: Grounds the AI’s responses in real documents you curate.
  • Respect Data Control: You choose which sources the AI can reference.
  • Supercharge Productivity: Agents can fetch facts, summarize docs, or cite answers right where your team works.

What RAG Cannot Do

  • Guarantee Perfect Accuracy: Outdated or poorly structured data can lead to incorrect answers.
  • Replace Human Judgment: It retrieves and summarizes but may miss subtle nuances or context.
  • Go Beyond Your Data Sources: It won’t invent domain-specific knowledge or fill gaps beyond your data.
  • Same Performance: Large datasets may slow the AI's generation or be expensive.

The Bottom Line

RAG lets AI agents connect with your actual business knowledge—making them more reliable, accurate, and useful for real, day-to-day work. But like any tool, it’s only as good as the data and workflows you set up. At Runbear, we’ve made RAG plug-and-play, so you can start small and scale as your needs grow. The most productive workflows come from combining RAG with the apps, channels, and knowledge your team already lives in.

Want to see RAG in action, or explore advanced use cases?

Let your agents finally “know what your team knows” so they can become real teammates.