#AI Model

Setup > AI Model is where you compare models before sending real shoppers to the widget.

#What this page is for

Use it to:

• Select the model HeiChat should use
• Compare token multipliers
• Test responses in the admin first
• Decide whether quality gains justify higher cost

#A practical testing workflow

#1. Define test questions

Prepare real questions from your store, such as:

• Product discovery
• Shipping and returns
• Order-related scenarios
• Edge cases that are important for your team

#2. Test more than one model

Try:

• Basic models for cost efficiency
• Advanced or premium models for higher accuracy

Compare:

• Accuracy
• Product recommendation quality
• Tone
• Token cost

#3. Fix setup issues before blaming the model

If answers are weak:

• Check Learned Products if product recommendations are missing
• Check Knowledge Base if store-specific information is missing

Many quality issues come from incomplete store data, not only from model choice.

#4. Roll out only after admin testing

For cautious launches:

1. Keep storefront activation off at first.
2. Test models and refine knowledge.
3. Activate the widget only after results are acceptable.

#Model cost logic

HeiChat labels models by tier and token multiplier relative to the baseline model.

As a rule:

• Lower multipliers are cheaper
• Higher multipliers often give better reasoning

Choose based on your actual traffic pattern, not only on lab-style comparisons.

#Custom OpenAI API key

If your plan supports it, you can configure a custom OpenAI API key from this page.

Use this only if your team already has a clear infrastructure or billing reason to manage AI traffic separately.