Human-in-the-Loop Feedback Systems
Design and implement systems where human feedback is integrated to continuously improve LLM performance and correct errors.
What is Human-in-the-Loop?
When working with Large Language Models (LLMs), sometimes their outputs aren't quite right. This is where Human-in-the-Loop (HITL) systems come in.
HITL means humans actively review, correct, or provide feedback on an LLM's output. This feedback then helps the model learn and improve over time, making it smarter and more reliable.
Why Humans Help LLMs
Even powerful LLMs have limitations. They can:
- Hallucinate: Make up facts.
- Be biased: Reflect biases from their training data.
- Lack up-to-date info: Not know about recent events.
- Misunderstand complex requests: Struggle with nuanced instructions.
Human oversight helps catch and fix these issues, ensuring higher quality and safer outputs.
All lessons in this course
- LLM Evaluation Metrics & Benchmarks
- Human-in-the-Loop Feedback Systems
- Prompt Injection & Security Best Practices
- Detecting & Mitigating Hallucinations