Lesson 5.2 – Human Feedback with raia Copilot

🎯 Learning Objectives

By the end of this lesson, you will be able to:

• Understand the role of human feedback in training and maintaining AI Agents

• Use raia Copilot to rate and correct Agent responses

• Identify the five common reasons an answer may be wrong

• Apply feedback loops to improve Agent performance over time

• Recognize how feedback contributes to Agent learning, refinement, and maturity

🧠 Why Human Feedback Matters

AI Agents do not learn the way software “bugs” are fixed. Instead of modifying code, we refine behavior through:

• Better training data

• Improved prompts

• Updated model instructions

• And most importantly: human feedback on how it’s performing in real-world conversations

Unlike hardcoded logic, AI is probabilistic. It needs frequent nudges in the right direction to refine its performance over time.

🔁 Reinforcement Learning (RLHF) Simplified

Reinforcement Learning from Human Feedback (RLHF) is how large language models are improved during training—and it's also how you improve your custom Agent.

Your job isn’t to rebuild the Agent every time it messes up—your job is to teach it through consistent review and feedback.

In raia, this is done with Copilot.

🧰 Using raia Copilot for Feedback

Copilot is the hands-on control center for reviewing Agent behavior:

• You see the full conversation

• You rate each answer as GOOD ✅ or BAD ❌

• For BAD responses, you:

  1. Provide a better version of the answer

  2. Tag the reason the answer was wrong

  3. Optionally add a comment

Each piece of feedback becomes part of the Agent’s evolving instructional layer and derivative training data.

📘 This feedback loop is essential for real-world readiness, as reinforced in [Module 7 – Beta Testing and Human Feedback Integration] and the [Reinforcement Learning and Continuous Improvement module].

❌ Five Common Reasons AI Gets It Wrong

When giving feedback, raia lets you tag the problem. Understanding why an answer is wrong is key to fixing it effectively.

1. 🧾 Bad Training

“The Agent gave a wrong or outdated answer.”

• Usually caused by missing or incorrect data in the training set

• Fix by updating source documents and retraining the vector store

📍Solution: Revisit training material and ensure facts are current, detailed, and chunked correctly.

2. 🧠 Hallucination

“The Agent confidently made up something false.”

• Occurs when the AI lacks real data and tries to guess or improvise

• More common with vague prompts or overly open-ended questions

📍Solution: Improve grounding (data), and use stronger system instructions to limit creative fabrication.

3. ✂️ Incomplete Answer

“It technically answered, but missed key details.”

• Common when the Agent truncates responses or uses boilerplate

• Often a sign that prompts or formatting aren’t encouraging deeper retrieval

📍Solution: Enhance prompt instructions, improve training examples, and ensure data chunks are informative.

4. 🔄 Out of Context

“The answer ignored context or misunderstood the question.”

• Can be caused by short, vague user prompts

• Also happens when context from earlier messages is dropped

📍Solution: Clarify user input and test for multi-turn conversational coherence.

5. 📉 Bad Prompt

“The user’s prompt was too short or unclear.”

• AI models rely heavily on how a question is asked

• Lazy or ambiguous questions lead to low-quality answers

📍Solution: Train users on better prompting OR structure instructions to handle weak inputs more gracefully.

✍️ Feedback Anatomy: What Makes Good Feedback?

💡 The more precise the correction, the faster the Agent improves.

🛠 Use Feedback for Two Critical Outcomes

1. Correction Teach the Agent what it should have said instead

2. Enrichment Add your corrected answer to the instructional memory or even training data → This builds long-term strength and avoids future repetition of the error

🔁 Feedback = Continuous Agent Maturity

Ongoing feedback enables:

• Better tone and empathy

• More accurate retrieval

• Fewer hallucinations

• Enhanced personalization over time

📝 Hands-On: Feedback Practice

In raia Copilot:

1. Load a real or simulated conversation

2. Read the Agent’s response

3. Rate it GOOD or BAD

4. If BAD:

   • Select the reason

   • Provide a better answer

   • Add a comment (optional but helpful)

🧪 Try testing a backdated support email and rating the Agent's proposed answer.

✅ Key Takeaways

• Human feedback is how Agents learn, adapt, and mature—just like a new employee

• raia Copilot makes it easy to give structured, actionable feedback

• Understanding why an answer is wrong helps you fix the root cause

• The best Agents are not built once—they are trained, tested, and tuned over time with real-world feedback