How to Use Knowledge Base (RAG)
What is Knowledge Base?
Knowledge Base is a library of files and URLs from which your AI bot can retrieve information. It helps to provide more accurate and helpful answers. Builders can upload and manage files here.
It uses a framework called Retrieval Augmented Generation (RAG).
What is Retrieval Augmented Generation (RAG)?
Retrieval Augmented Generation (RAG) basically allows LLMs - which are trained on general knowledge from the Internet - to access your organization’s data and knowledge.
Knowledge Base Retrieval Types
Top K: A variable that sets the number of top chunks to be retrieved. A higher 'K' value means more chunks are retrieved, which can improve accuracy but may slow down response time.
Retrieval type: This allows you to determine how information is retrieved from the selection in the dropdown
- 'Chunk' retrieves specific sections that are the most similar to the user prompt.
- 'Neighbor' retrieves related content that is most similar to the user prompt, and retrieves 1 chunk before and 1 chunk after.
- 'Document' retrieves the entire document for context.
When to use which retrieval method?
- Chunk: This is best used when the answer to a user's query might be found in a small section of text within a larger document.
- Neighbor: This is useful when LLM needs more context surrounding the direct answer to the user query.
- Document: This works best when the user query requires LLM to understand the entire documents.
- When experimenting with different retrieval methods, pay attention to quality of output, latency, and context window limit.
Keep Reading
Understanding Rate Limits on CreateAI Builder
Kofi Wood and Shailee Shah
Ever run into a message that says, “Your project has reached its Tokens per Minute limit (TPM)”? Well, just think of it as a friendly traffic signal reminding us not to zoom too fast. We’ll walk through what does a token mean, what does reaching TPM mean, which settings affect token usage, and how to optimize your AI Project to avoid hitting the limit.
AI with Integrity: ASU’s AI Acceleration Team is Setting New Standards for Ethical AI
Faith Timoh Abang
Artificial intelligence (AI) is rapidly transforming industries, from healthcare and finance to entertainment and education. At Arizona State University (ASU), the AI Acceleration team within Enterprise Technology is ensuring that this transformation happens responsibly.
Generative AI Tool Pre-Release Evaluation Guide
Stella Wenxing Liu
Arizona State University remains dedicated to responsible, principled innovation when deploying generative AI solutions, including chatbots. This guide ensures each project aligns with ASU’s values by mitigating potential risks—such as misinformation, bias, toxicity, and compliance lapses—using rigorous methods like automated testing, red teaming, and pilot experiments. In doing so, we uphold accuracy, fairness, and user trust while enhancing digital experiences across the university.
Agents in Generative AI
Zohair Zaidi
Agents in generative AI are semi-autonomous entities that collaborate and interact dynamically, allowing them to solve complex problems and combine specialized capabilities for greater efficiency and adaptability.