How to Evaluate a RAG-Based AI Assistant When You're Not a Technical Team

As AI-powered assistants become more common, many teams—especially in support, product, or documentation—are being asked to evaluate complex AI systems like RAG (Retrieval-Augmented Generation). But what if you’re not a technical team? How do you know if the AI is actually helping your users or just making things up?

Here’s a simple, structured framework to help non-technical teams confidently evaluate a RAG assistant, focusing on accuracy, usefulness, and trust—without needing to know how the AI works under the hood.

✅ Step 1: Build a Realistic Test Set

Start by gathering 20–50 real user questions that your documentation should be able to answer. These can come from:

• Search logs or top queries from your help site

• Common support tickets or live chat questions

• Your team’s subject matter expertise

Make sure the questions are clear and representative of what users actually want to know and that the answer can actually be found on the documentation.

🧪 Step 2: Review AI Answers Using a Simple Rubric

Once the AI assistant provides answers to your test questions, evaluate each one using this 5-point scoring system across five key categories:

Accuracy: Is the answer factually correct based on your documentation?

Relevance: Does it directly address the question asked?

Clarity: Is the answer easy to understand and well-written?

Helpfulness: Would this response actually help a user solve their problem?

Confidence: Would you feel comfortable sharing this answer with a customer?

Use a spreadsheet to track each answer and score them 1 (poor) to 5 (excellent) for each category. This helps you get a clear, comparable picture across multiple responses.

🔍 Step 3: Tag Common Issues

While scoring, also tag any issues you see, such as:

• ❌ Hallucinations: The AI makes up facts or features not in your docs.

• 📚 Not grounded: The answer seems correct, but isn’t clearly based on your content.

• ❓ Vagueness: The answer is too generic or unhelpful.

• ⏳ Latency: It takes too long to respond.

• 🔄 Outdated info: The AI gives answers based on older versions of your content.

Tracking these helps you understand why scores are low and whether the system can be trusted.

📊 Step 4: Analyze and Decide

Once all answers are reviewed:

• Average your scores across all questions to create a baseline.

• Look for patterns (e.g., is it consistently vague? does it hallucinate often?).

• Ask yourself:

  • Is this better than our current support experience?

  • What percentage of answers are “good enough” to share with users?

  • Do we trust this to go live?

You don’t need perfection—but you do need consistency, factual accuracy, and trustworthiness.

🎯 Bonus Tip: Involve Multiple Reviewers

If possible, invite different roles to score and review answers:

• Support agents can assess helpfulness and completeness.

• Content writers can assess clarity and style.

• Product/tech experts can check factual accuracy.

Multiple perspectives lead to more balanced feedback—and higher confidence in your final evaluation.