Mitigating Hallucinations and Bias
Implement strategies to reduce LLM hallucinations and address potential biases in both retrieved documents and generated responses.
Taming RAG's Wild Side
Welcome! In this lesson, we'll tackle two big challenges in RAG systems: hallucinations and bias. Understanding and mitigating these is key to building trustworthy AI applications.
Hallucinations are when an LLM generates information that isn't factual or supported by its given context. Bias refers to outputs that unfairly favor or disfavor certain groups or ideas.
Why RAG Still Hallucinates
You might think RAG prevents hallucinations by providing facts. While it helps, they can still occur if:
- Poor Retrieval: The system fails to find truly relevant documents.
- Insufficient Context: Retrieved information is too sparse or contradictory.
- LLM Misinterpretation: The LLM misunderstands the provided context.
- Context Window Limits: The LLM prioritizes its internal knowledge when context is truncated.
All lessons in this course
- Data Privacy and PII Handling
- Mitigating Hallucinations and Bias
- Responsible AI Practices for RAG
- Defending Against Prompt Injection