Magento 2 Module - Ask About a Product

Kowal_ZapytajOProdukt is an advanced Magento 2 module for customer communication on the product page. It combines a classic inquiry form, structured product FAQ, and an AI Assistant into one consistent solution.

In practice, this means the product page stops being only a place to display descriptions and technical parameters and becomes an active customer support touchpoint. The user can:

• ask a standard product question,
• use ready-to-publish answers in the FAQ,
• chat with an AI Assistant that responds in the context of the currently viewed product.

The module is designed to solve two problems at the same time:

• support operations: reduce repetitive questions reaching the store team,
• product knowledge: build a growing, structured knowledge base that improves answer quality over time.

Business goal

In many online stores, a large portion of customer questions repeats:

• whether the product is compatible with a specific Magento version,
• whether it works without an additional module,
• how installation works,
• whether it supports multiple languages,
• whether it requires custom theme changes,
• how it behaves in a specific business scenario.

Without a dedicated tool, such questions:

• increase support workload,
• slow down purchasing decisions,
• scatter knowledge across email inboxes, tickets, and sales conversations,
• do not return to the storefront as a structured FAQ.

This module organizes the process. First it collects questions and answers, then structures them into an FAQ, and at the next stage uses them as context for the AI Assistant and a retrieval layer based on OpenAI Vector Store.

Core solution concept

The module works in layers.

Layer 1. Classic product inquiries

On the product page you can enable a standard inquiry mechanism. The customer sends a question, and the administrator or store staff receives it for further handling. This is the simplest and most predictable form of contact.

Layer 2. FAQ

Recurring questions and answers can be saved and published as a product FAQ. The FAQ can be displayed as a tab or as a separate section on the product page. This way, future visitors get an answer without sending a new question.

Layer 3. AI Assistant

Above or below the standard FAQ, a lightweight AI chat component appears. The user can:

• click one of the popular questions,
• type their own question in the Ask the Assistant about this product. field,
• see the answer in the same chat area.

The Assistant is not a general store chatbot. It is designed as a product assistant, which means the answer should be based primarily on:

• current product data,
• published FAQ,
• current conversation history,
• optionally retrieval results from OpenAI Vector Store.

Module feature scope

1. Ask About a Product form

The module provides a classic customer inquiry mechanism.

Key elements:

• a button or Ask About a Product form on the product page,
• AJAX support on the frontend,
• saving questions to the database,
• optional email notification sending,
• option to enable the module globally or only for selected products.

This still makes sense even if the store already uses the AI Assistant. Not every question should be handled automatically. Some cases require a sales response, a custom quote, or confirmation by a technical team.

2. Product page FAQ

The FAQ in this module is not a marketing add-on, but a structured product knowledge layer.

The administrator can:

• review saved questions,
• add answers,
• publish selected records,
• display them on the product page.

The FAQ can be shown:

• as a tab,
• as a separate section on the product page.

Importantly, the FAQ is not only for the frontend. Published questions and answers are also used as one of the most important context sources for the AI Assistant.

3. AI Assistant on the product page

The AI Assistant is the central element of the module expansion.

The component is embedded on the product page, by default under the gallery, and was designed to:

• run lightweight on the frontend,
• avoid unnecessary load on the initial page render,
• remain readable on desktop and mobile,
• be ready for further extension.

The user sees:

• section title,
• intro text,
• a single text input for asking a question,
• a list of most popular questions,
• a chat area that grows with subsequent questions and answers.

In the current version the form also supports:

• in-session conversation history,
• clickable popular questions,
• AI answer feedback,
• two color variants: light and dark.

4. Popular questions

Below the text input, the most popular product questions can be displayed.

This solution serves multiple functions at once:

• speeds up conversation start,
• suggests what other customers ask most often,
• reuses ready FAQ answers without the cost of an AI model request,
• improves UX and reduces empty interactions.

Question popularity is no longer based only on manual ordering. The module collects click, question, and feedback data, and then ranks the FAQ accordingly.

5. AI answer context

The most important design assumption was that AI should not answer outside the product context.

The answer can be built from multiple sources:

• core product data,
• short description,
• full description,
• selected product attributes,
• published FAQ,
• conversation history.

Additionally, the module lets you limit which attributes are sent to the model, helping avoid:

• prompt overload,
• sending unnecessary data,
• excessive token cost,
• accidentally passing content that is not useful to the customer.

6. Integration with OpenAI Responses API and Vector Store

One of the key expansion elements is integration with OpenAI Responses API.

In simpler scenarios, the module can run using local product and FAQ context. In more advanced deployments it supports:

• file_search,
• vector_store_ids,
• filtering by sku,
• filtering by product_sku,
• filtering by store_code,
• filtering by content_type,
• limiting the number of retrieval results,
• hybrid mode,
• retrieval-first mode.

This means the AI answer can rely not only on data sent directly from Magento in the current request, but also on documents previously ingested into Vector Store.

In practice this provides two benefits:

• lower cost, because you do not need to send the full data set to the model each time,
• better scalability, because retrieval can handle a larger knowledge base than a simple prompt with local JSON.

7. Integration with Kowal_AiProductFeed

The module is prepared to work with Kowal_AiProductFeed.

This integration allows you to:

• synchronize product data to OpenAI Vector Store,
• use documents such as product.core, product.faq, product.docs, and others,
• sync a specific product right before the chat,
• limit retrieval to specific content types.

This approach is especially useful where:

• product descriptions are long,
• the FAQ is extensive,
• the store handles many technical products,
• product data is continuously evolving.

8. Analytics and feedback

The module does not end with generating an answer.

It also stores data that helps evaluate whether the solution works:

• number of FAQ clicks,
• number of submitted questions,
• helpful or not helpful ratings,
• conversation history,
• technical metadata of the AI response,
• token usage,
• request and response payloads when diagnostic logging is enabled.

This makes the deployment not a black box. The team can analyze:

• which questions appear most often,
• whether AI uses retrieval,
• whether answers are accurate,
• which records are worth turning into FAQ,
• how cost and quality change after prompt or configuration adjustments.

9. FAQ candidates and admin workflow

One of the most important advantages of the module is the ability to convert conversations into new FAQ entries.

The process is as follows:

1. Customers ask questions.
2. The module stores conversations.
3. The analysis mechanism identifies FAQ candidates.
4. The administrator reviews candidates in the admin panel.
5. After approval, the candidate is added to the standard product FAQ.

This is a very practical workflow model, because knowledge does not get lost in chat history. With each iteration the store builds a better response layer:

• for customers,
• for the FAQ,
• for the AI Assistant,
• for future retrieval.

10. Security and control

The module was built so its behavior can be controlled.

Configuration includes, among others:

• guest access restrictions,
• chat TTL,
• rate limits,
• input data sanitization,
• diagnostic logging options,
• reCAPTCHA configuration,
• controlled scope of data sent to the model.

This matters because implementing AI on the product page should not mean losing control over:

• cost,
• data,
• answer quality,
• frontend performance.

11. Who this module is for

The module is a strong fit for projects where:

• the catalog is larger than a few simple products,
• customers often ask about compatibility, configuration, or implementation,
• the team wants to combine classic FAQ with a modern AI layer,
• the company develops product documentation and wants to use it for retrieval,
• control over what the AI knows and where it gets answers is important.

It fits especially well for stores selling:

• Magento extensions,
• technical products,
• B2B solutions,
• tools requiring implementation or configuration,
• products where customers expect a fast and precise answer before purchase.

12. Summary

Kowal_ZapytajOProdukt is no longer only a module for a simple contact form on the product page.

It is a complete product communication layer that:

• collects questions,
• publishes FAQ,
• answers via AI,
• uses Vector Store,
• analyzes conversations,
• and turns them into an increasingly better store knowledge base.

As a result, the product page becomes a place for real conversation with the customer, not just a static page with a description and price.