FeedGen: Optimise Shopping feeds with Generative AI

Disclaimer: This is not an official Google product.

Overview • Get started • What it solves • How it works • How to Contribute

Overview

FeedGen is an open-source tool that uses Google Cloud's state-of-the-art Large Language Models (LLMs) to improve product titles, generate more comprehensive descriptions, and fill missing attributes in product feeds. It helps merchants and advertisers surface and fix quality issues in their feeds using Generative AI in a simple and configurable way.

The tool relies on GCP's Vertex AI API to provide both zero-shot and few-shot inference capabilities on GCP's foundational LLMs. With few-shot prompting, you use the best 3-10 samples from your own Shopping feed to customise the model's responses towards your own data, thus achieving higher quality and more consistent output. This can be optimised further by fine-tuning the foundational models with your own proprietary data. Find out how to fine-tune models with Vertex AI, along with the benefits of doing so, at this guide.

Note: Please check if your target feed language is one of the Vertex AI supported languages before using FeedGen, and reach out to your Google Cloud or Account representatives if not.

Get Started

To get started with FeedGen:

Make a copy of the Google Sheets spreadsheet template
Follow the instructions detailed in the Getting Started worksheet

Challenges

Optimising Shopping feeds is a goal for every advertiser working with Google Merchant Center (MC) in order to improve query matching, increase coverage, and ultimately click-through rates (CTR). However, it is cumbersome to sift through product disapprovals in MC or manually fix quality issues.

FeedGen tackles this using Generative AI - allowing users to surface and fix quality issues, and fill attribute gaps in their feeds, in an automated fashion.

Solution Overview

FeedGen is an Apps Script based application that runs as an HTML sidebar (see HtmlService for details) in Google Sheets. The associated Google Sheets spreadsheet template is where all the magic happens; it holds the input feed that needs optimisation, along with specific configuration values that control how content is generated. The spreadsheet is also used for both (optional) human validation and setting up a supplemental feed in Google Merchant Center (MC).

Generative Language in Vertex AI, and in general, is an experimental feature / technology. We highly recommend manually reviewing and verifying the generated titles and descriptions. FeedGen helps users expedite this process by providing a score between -1 and 1 (along with detailed components) that represents how "good" the generated content is, along with a Sheets-native way for bulk-approving generated content via data filters.

First, make a copy of the template spreadsheet and follow the instructions defined in the Getting Started section. The first step to authenticate to the Apps Script environment via the Initialise button as shown below.

Afterwards, navigate to the Config worksheet to configure feed settings, Vertex AI API settings, and settings to control the content generation.

All available data is used to generate descriptions, while titles use few-shot prompting; a technique where one would select samples from their own input feed as shown below to customise the model's responses towards their data. To help with this process, FeedGen provides a utility Google Sheets formula:

=FEEDGEN_CREATE_CONTEXT_JSON('Input Feed'!A2)

Which can be used to fill up the “Context” information field in the few-shot prompt examples table by dragging it down, just as for other Sheets formulas. This "Context" represents the entire row of data from the input feed for this item, and it will be sent as part of the prompt to the Vertex AI API.

Afterwards, you must manually fill in the remaining columns of the few-shot prompt examples table, which define the expected output by the LLM. These examples are very important as they provide the basis upon which the LLM will learn how it should generate content for the rest of the input feed. The best examples to pick are products where:

You can identify attributes in the existing title that match column names from your input feed.
You can propose a new title that is better than the existing one (e.g. contains more attributes).
The proposed title has a structure that can be reused for other products within your feed.

We would recommend adding at least one example per unique category within your feed, especially if the ideal title composition would differ.

FeedGen defaults to using attributes from the input feed instead of generated attribute values for composing the title, to avoid LLM hallucinations and ensure consistency. For example, the value Blue from the input feed attribute Color for a specific feed item will be used for its corresponding title instead of, say, a generated value Navy. This behaviour can be overridden with the Prefer Generated Values checkbox in the Advanced Settings section of the Title Prompt Settings, and is useful whenever the input feed itself contains erroneous or poor quality data.

Within this same section you can also specify a list of attribute keys that should not be used for composing the generated titles. Examples are attributes like Price or Promotion, which should not be part of titles at all as per the Best Practices outlined by MC. Additionally, you can specify a list of safe words that can be output in generated titles even if they did not exist beforehand in your feed. For example, you can add the word "Size" to this list if you would like to prefix all values of the Size attribute with it (i.e. "Size M" instead of "M").

Now you are done with the configuration and ready to optimise your feed. Use the top navigation menu to launch the FeedGen sidebar and start generating and validating content in the Generated Content Validation worksheet.

You would typically work within this view to understand, approve and/or regenerate content for each feed item as follows:

The Generate button controls the generation process, and will first regenerate all columns with an empty or failed status before continuing on with the rest of the feed. For example, clearing row 7 above and clicking Generate will start the generation process at row 7 first, before continuing on with the next feed item.
- This means that you may clear the value of the Status column for any feed item in order to regenerate it.
- To start from scratch, click Clear Generated Data first before Generate.
If an error occurs, it will be reflected in the Status column as "Failed".
Approval can be done in bulk via filtering the view and using the Approve Filtered button, or individually using the checkboxes in the Approval column. All entries with a score above 0 will already be pre-approved (read more about FeedGen's Scoring / Evaluation system below).
Additional columns for titles and descriptions are grouped, so that you may expand the group you are interested in examining.

Once you have completed all the necessary approvals and are satisfied with the output, click Export to Output Feed to transfer all approved feed items to the Output Feed worksheet.

The last step is to connect the spreadsheet to MC as a supplemental feed, this can be done as described by this Help Center article for standard MC accounts, and this Help Center article for multi-client accounts (MCA).

Notice that there is a att-p-feedgen column in the output feed. This column name is completely flexible and can be changed directly in the output worksheet. It adds a custom attribute to the supplemental feed for reporting and performance measurement purposes.

Scoring / Evaluation

FeedGen provides a score for generated titles between -1 and 1 that acts as a quality indicator. Positive scores indicate varying degrees of good quality, while negative scores represent uncertainty over the generated content.

Let's take a closer look with some fictitous examples:

Original Title: 2XU Men's Swim Compression Long Sleeve Top
Original Description: 2XU Men's Swim Compression Long Sleeve Top, lightweight, breathable PWX fabric, UPF 50+ protects you from the sun.
Generated Title: 2XU Men's Swim Compression Long Sleeve Top Black Size M PWX Fabric UV Protection
Score: -1
Reasoning: New words, namely "UV Protection", were added that may have been hallucinated by the language model. Indeed, "UV Protection" is not explicitly mentioned anywhere in the input feed. Examining the feed item more clearly however surfaces that the description contains the value UPF 50+, so the addition of UV Protection is actually a positive thing, but since we have no way of assessing this (without applying a more granular semantic text analysis) we default to penalising the score.

Let's look at another example for the same product:

Original Title: 2XU Men's Swim Compression Long Sleeve Top
Generated Title: 2XU Men's Swim Compression Top Size M
Score: -0.5
Reasoning: Key information was removed from the title, namely "Long Sleeve", which describes the type of the product. FeedGen identifies this information by first examining the structure of titles via what we refer to as templates in our uniquitous language, before diving deeper and comparing the individual words that compose the titles. Let's check the templates for our example:
- Original Title Template: <Brand> <Gender> <Category> <Product Type>
- Generated Title Template: <Brand> <Gender> <Category> <Product Type> <Size>
- As you can see no attributes were actually removed, but rather the components of the Product Type attribute changed in a worse way, hence the negative score.

FeedGen is conservative in its scoring; it will assign a score of -0.5 whenever any words get removed, even if those words were promotional phrases such as get yours now or while stocks last, which should not be part of titles as per the Best Practices outlined by Merchant Center (MC).

Alright, so what makes a good title? Let's look at another example:

Original Title: 2XU Men's Swim Compression Long Sleeve Top
Generated Title: 2XU Men's Swim Compression Long Sleeve Top Size M
Score: 0.5
Reasoning: Nothing was changed or lost in the original title, and we added a new attribute, "Size". If the product was being offered in different sizes, this new title would now be vital in preventing all feed items for the product from getting rejected by MC (due to their titles being duplicates).

Finally, what's the ideal case? Let's take a look at one last example:

Original Title: 2XU Men's Swim Compression Long Sleeve Top
Input - Color: missing
Generated Title: 2XU Men's Swim Compression Long Sleeve Top Black Size M
Output - Color: Black
Score: 1
Reasoning: This is the best possible scenario; we optimised the title and filled feed attribute gaps along the way, a score of 1 is definitely well-deserved.

So in summary, the scoring systems works as follows:

Are there hallucinations?	Have we removed any words?	No change at all?	Have we optimised the title?	Did we fill in missing gaps or extract new attributes?
-1	-0.5	0	Add 0.5	Add 0.5

FeedGen also applies some basic MC compliance checks, such as titles and descriptions must not be longer than 150 and 5000 characters, respectively. If the generated content fails these checks, the value Failed compliance checks will be output in the Status column. As mentioned above, FeedGen will attempt to regenerate Failed items first whenever the Generate button is clicked.

Feed Gaps and New Attributes

FeedGen does not just fill gaps in your feed, but might also create completely new attributes that were not provided in the Input Feed. This is controllable via the few-shot prompting examples in the Config sheet; by providing "new" attributes that do not exist in the input within those examples, FeedGen will attempt to extract values for those new attributes from other values in the input feed. Let's take a look at an example:

Original Title	Product Attributes in Original Title	Product Attributes in Generated Title	Generated Attribute Values
ASICS Women's Performance Running Capri Tight	Brand, Gender, Product Type	Brand, Gender, Product Type, Fit	ASICS, Women's Performance, Running Capri, Tight

Notice here how the Fit attribute was extracted out of Product Type. FeedGen would now attempt to do the same for all other products in the feed, so for example it will extract the value Relaxed as Fit from the title Agave Men's Jeans Waterman Relaxed. If you do not want those attributes to be created, make sure you only use attributes that exist in the input feed for your few-shot prompting examples. Furthermore, those completely new feed attributes will be prefixed with feedgen- in the Output Feed (e.g. feedgen-Fit) and will be sorted to the end of the sheet to make it easier for you to locate and delete should you not want to use them.

Best Practices

We recommend the following patterns for titles according to your business domain:

Domain	Recommended title structure	Example
Apparel	Brand + Gender + Product Type + Attributes (Color, Size, Material)	Ann Taylor Women’s Sweater, Black (Size 6)
Consumable	Brand + Product Type + Attributes (Weight, Count)	TwinLab Mega CoQ10, 50 mg, 60 caps
Hard Goods	Brand + Product + Attributes (Size, Weight, Quantity)	Frontgate Wicker Patio Chair Set, Brown, 4-Piece
Electronics	Brand + Attribute + Product Type	Samsung 88” Smart LED TV with 4K 3D Curved Screen
Books	Title + Type + Format (Hardcover, eBook) + Author	1,000 Italian Recipe Cookbook, Hardcover by Michele Scicolone

You can rely on these patterns to generate the few-shot prompting examples defined in the FeedGen Config worksheet, which will accordingly steer the values generated by the model.

We also suggest the following:

Provide as many product attributes as possible for enriching description generation.
Use size, color, and gender / age group for title generation, if available.
Do NOT use model numbers or promotional text in titles.

Vertex AI Pricing and Quotas

Please refer to the Vertex AI Pricing and Quotas and Limits guides for more information.

How to Contribute

Beyond the information outlined in our Contributing Guide, you would need to follow these additional steps to build FeedGen locally:

Make sure your system has an up-to-date installation of Node.js and npm.
Navigate to the directory where the FeedGen source code lives.
Run npm install.
Run npx @google/aside init and click through the prompts.
- Input the Apps Script Script ID associated with your target Google Sheets spreadsheet. You can find out this value by clicking on Extensions > Apps Script in the top navigation menu of your target sheet, then navigating to Project Settings (the gear icon) in the resulting Apps Script view.
Run npm run deploy to build, test and deploy (via clasp) all code to the target spreadsheet / Apps Script project.

gernberg / feedgen