Choose the Right Business AI Model with a 20-Question Test

Choosing an AI model by reputation, price or a polished demo is an expensive way to start. The model that writes excellent marketing copy may still give unreliable answers about your returns policy, product catalogue or internal processes.

This week, you can make the decision more practical: build a small evaluation set from real business questions, then use it to compare models and retrieval approaches. You do not need a large dataset or a six-month AI project. You need representative questions, clear success criteria and a controlled test.

Start with 20 real questions

Create a list of questions your team, customers or sales prospects actually ask. Avoid questions written to make the AI look good. Include the messy, incomplete and uncomfortable ones.

For example, an e-commerce business might collect questions such as:

• “Can I return a sale item bought three weeks ago?”
• “Which sofa fabric is easiest to clean if I have pets?”
• “Is this product available in the Dubai store?”
• “Summarise the delivery rules for orders over a certain value.”
• “I need a replacement part, but I do not know its product code.”

Aim for 20 to 30 questions across the main use case. If you are building an internal assistant, ask employees for common questions from email, WhatsApp, support tickets or shared documents. If you are building a customer-facing assistant, use anonymised questions from conversations and search data.

Label each question by type:

• Known fact: The answer should be directly available in a document or database.
• Multi-step: The assistant must combine information from more than one source.
• Ambiguous: The assistant should ask a clarifying question.
• Out of scope: The assistant should explain that it cannot help.
• Sensitive: The assistant must avoid exposing private or restricted information.

This classification matters because a useful system is not one that answers every question. It is one that answers the right questions, asks for clarification when needed and refuses safely when the information is missing.

Define what “good” means before testing

Do not compare models using a general impression such as “Model A feels smarter.” Write down the behaviour you need for each question.

For every test, record:

1. Expected answer or source: What should the assistant say, or which document should support the answer?
2. Must-include details: For example, a return time limit, a required form or a delivery restriction.
3. Must-not-do details: For example, invent a discount, reveal an internal margin or promise a delivery date.
4. Acceptable fallback: What should the assistant say if the answer is not available?
5. Escalation rule: When should the question go to a person?

A simple scoring system is enough for the first round. Give each answer a score from 0 to 2:

• 0: Incorrect, unsafe or confidently invented.
• 1: Partly useful but missing an important detail or requiring correction.
• 2: Correct, appropriately supported and written in the desired style.

You can also track response time and approximate cost per question. These are examples, not universal targets. A customer-facing assistant may need fast answers, while an internal research tool may tolerate a slower response if the answer is more reliable.

Test three setups, not just three models

Businesses often focus on choosing the model while ignoring the system around it. In practice, the same model can perform very differently depending on the instructions, document retrieval and output controls.

Run your 20 questions through three basic setups:

1. Model-only baseline

Give the model your instructions but no private company documents. This shows what it already knows and where it is likely to guess.

The baseline is useful even if you know you will need private data. It helps you see whether a question requires retrieval at all, and it gives you a comparison point for later improvements.

2. RAG with a small, clean document set

Connect only the documents needed for the selected use case. This could include a current returns policy, delivery guide, product information and escalation procedure.

RAG, or retrieval-augmented generation, allows the system to find relevant passages from your private data and use them when forming an answer. It does not automatically make poor documents reliable. If two files contain different return periods, the assistant may still produce a confused answer unless you establish which source is current.

Before testing, remove duplicates, archive outdated versions and add clear titles and dates. A smaller, well-maintained knowledge base is usually easier to evaluate than a large folder containing everything the company has ever written.

3. RAG with instructions and guardrails

Add rules for source use, uncertainty and escalation. For example:

• Answer policy questions only from approved documents.
• If the retrieved material does not support an answer, say so.
• Never create a price, stock level, delivery promise or refund decision.
• Ask for the order number only through an approved secure workflow.
• Send complaints, legal questions and exceptions to a human.

This third test shows the difference between “the model knows the answer” and “the business can safely use the system.”

Use the results to make a practical decision

Review the failures by category rather than choosing the setup with the best average score. One dangerous hallucination may matter more than several small style problems.

If the model-only baseline performs well on general questions but fails on policy details, a retrieval layer is likely necessary. If RAG returns irrelevant or conflicting passages, improve the documents and retrieval settings before switching models. If the answers are accurate but the assistant takes too long or costs too much, test a smaller model for simple questions and reserve a stronger model for complex cases.

A useful routing policy might look like this:

• Use a lower-cost model for classification, formatting and simple lookups.
• Use a stronger model for multi-document questions or nuanced drafting.
• Require retrieval for private facts.
• Escalate low-confidence, sensitive or high-impact requests.

For example, if a store receives 300 customer questions a week, you could route opening-hours questions through a lightweight flow while sending product compatibility questions to a retrieval-enabled workflow. The numbers and routing rules would depend on your business; the principle is to match cost and capability to the task.

A one-week implementation checklist

Day 1: Collect the questions

Gather 20 to 30 real, anonymised questions and label their types. Include at least a few out-of-scope and ambiguous examples.

Day 2: Prepare the sources

Choose the documents that should support the answers. Remove obsolete versions and identify the owner responsible for keeping each source current.

Day 3: Write the acceptance rules

Define correct answers, prohibited behaviour, fallback language and escalation triggers. Keep the rules specific enough for another person to apply consistently.

Day 4: Run the comparison

Test the model-only, RAG and guarded RAG setups. Record answers, sources, failures, response time and estimated cost.

Day 5: Decide the first production scope

Choose one narrow workflow with clear boundaries. Document which model, sources, guardrails and human hand-off rules will be used. Keep the evaluation set as a regression test for every future change.

How ADMOV can help

ADMOV helps businesses set up and integrate LLM systems around real workflows, not generic chatbot demos. We can help you turn your business questions into an evaluation set, clean and connect private documents through a RAG setup, define guardrails and escalation paths, and compare model options against reliability and cost requirements.

The same test set can then become an ongoing quality check as your documents, prompts or models change. Book a free call at https://admov.io/#contact to discuss the first workflow you want to evaluate.