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AI Generated ContentThis document was generated by an AI Agent on Teammately. When developing AI on Teammately, you can also generate, share and publish documents like this.Product Category Classifier
About this AI
Summary
The Product Categorization is designed to efficiently classify products based on detailed descriptive inputs from users. Leveraging advanced large language models (LLMs), this AI tool automates product categorization for e-commerce platforms, reducing the need for manual entry. By utilizing sophisticated prompt engineering and validation techniques, it ensures that the categorizations are not only accurate but also reliable and consistent. Ultimately, this enhances operational efficiency and enriches user experience by enabling seamless categorization suited for various e-commerce applications.
Major Use Cases
E-commerce Integration: Automatically categorize new product listings based on user descriptions.
Inventory Management: Streamline product categorization for warehouses to enhance inventory tracking.
Search Optimization: Improve search and recommendation engines by categorizing products correctly.
User Experience: Enhance UX by providing accurate categories for personalized suggestions.
Quality Assurance: Validate product data to reduce errors in classification.
Milestone
PRD Completion: We have successfully drafted the Product Requirements Document (PRD) that outlines the strategic framework for developing the AI agent.
AI Development: We have built the AI architecture and logic, focusing on product analysis and categorization using LLMs.
Initial Testing: We have conducted quick tests to verify the AI's functionality, ensuring the model categorizes products as intended with high accuracy and consistency.
AI Architecture & Logic Plans
AI Plans
API INPUT KEYS
descriptionText
STEPS
Initial Description Parsing
Model
openai / gpt-4oPrompt
```text
## Objective
Your task is to analyze the provided product description and generate a list of potential categories by identifying key attributes such as product type, features, and differentiators. This will ensure broad categorization and help eliminate unrelated categories.
## Instructions
1. Read the product description carefully.
2. Extract relevant keywords related to the product's type, primary use, material, and unique characteristics.
3. Generate a broad list of potential categories based on the identified keywords and context.
4. Focus on suggesting categories that cover a wide range yet remain relevant to the description.
## Product Description
{{description}}Value of the API input "description" is inserted
## Example
### Description: "This is a blue leather wallet with RFID protection."
- Accessories > Wallets
- Security > RFID Protection Products
- Fashion > Leather Goods
## Output
Please provide the list of potential categories as plain text, each prefixed by a bullet point without any additional description or explanation.
```
Contextual Data Analysis
Model
openai / gpt-4oPrompt
## Contextual Data Analysis
"Analyze the product description for additional contextual information, focusing on material, usage, and target audience. Utilize this information to refine the initial category suggestions."
### Initial Category Suggestions
- {{loPhopbPSQK921UYGZsTPQ}}Value of the result from the step "Initial Description Parsing" is inserted
### Example
Product Description: "This athletic jacket is made with breathable fabric, ideal for outdoor activities and suitable for both men and women."
Refined Category: `{ "refined_category": "Apparel > Outerwear > Activewear Jackets" }`
### Expected Output Format
Provide the refined category in the format:
`{ "refined_category": "<Refined Category>" }`
Guidance: Ensure clarity and precision in the refined category. Do not include any unnecessary prefix or explanation in your output.
## Output
Verification and Consistency Check
Model
openai / gpt-4oPrompt
## Verification and Consistency Check
### Context Analysis
Given the parsed product description and contextual data:
- **Initial Parsing Results**: {{loPhopbPSQK921UYGZsTPQ}}Value of the result from the step "Initial Description Parsing" is inserted
- **Contextual Data**: {{TkWDTlaHRo2NcH82pQUc6w}}Value of the result from the step "Contextual Data Analysis" is inserted
Analyze the product based on the above information, focusing on key attributes like material, usage, and target audience.
### Justification Generation
For each suggested category from the previous analysis:
- Generate an explanation as to why the category is appropriate.
- Base your justification on identified attributes such as material, usage, and target audience.
### Consistency Check
- Review each suggested category for consistency with the product's features.
- Ensure alignment with previously identified characteristics to validate its suitability.
## Output
Provide a textual justification for each category suggestion ensuring consistency with product features. Avoid unnecessary prefixes or verbose descriptions.
API OUTPUT KEYS
categoryVerification and Consistency CheckText
Here are several examples of the input and output of this model.
Quick Test
Input
AI Synthesized
description
Run
3 steps16,524 ms
Output
from your model in draft
category
### Suggested Categories and Justifications
1. **Electronics > Smart Speakers**
- **Justification**: Smart speakers fall under the electronics category due to their technological functionality. The emphasis on "smart" capabilities highlights features like voice control, which is a primary attribute of smart speakers. Their integration with digital assistants makes them suitable for this category.
2. **Home Automation > Voice-Controlled Devices**
- **Justification**: Products that enable users to control aspects of their home environment through voice commands fit naturally here. The product's capability to interface with home automation systems and respond to voice inputs supports its classification as a voice-controlled device.
3. **Audio > High-Fidelity Audio Equipment**
- **Justification**: If the product emphasizes superior sound quality, it belongs in the high-fidelity audio category. Features such as enhanced acoustics, sound clarity, and high-definition audio output justify its placement under this audio-focused classification.
4. **Smart Home > Compatible Devices**
- **Justification**: A smart speaker designed to interact seamlessly with other smart home technologies should be classified as a compatible device. Its ability to work with existing smart home ecosystems, enhancing the user's experience, aligns with this category.
### Consistency Check
- The classification as **Electronics** and **Smart Speakers** aligns with the product's technology and smart features.
- The **Home Automation** classification aligns given the voice control and integration capabilities.
- Placement under **Audio** products aligns if sound quality is a primary feature; otherwise, it may not fit.
- The **Smart Home** category is appropriate if there is substantial integration with smart home devices.
Each category fits well with at least one of the product's characteristics, ensuring an accurate and consistent categorization based on the provided attributes.
Quick Evaluation by LLM Judges
Metric
Categorization Accuracy
Score
Reason
The model response accurately categorizes the smart home speaker into several relevant categories (Electronics > Smart Speakers, Home Automation > Voice-Controlled Devices, Audio > High-Fidelity Audio Equipment, Smart Home > Compatible Devices). The justification for each category is well-reasoned and directly links the speaker's features (voice control, high-fidelity audio, home automation compatibility) to the category. The response also includes a consistency check, verifying the alignment of the categories with the product's characteristics. This demonstrates a high degree of accuracy in matching the product description to appropriate categories, fulfilling the requirements for a Grade 2. Expand
Metric
Prompt Effectiveness for Accurate Product Categorization
Score
Reason
The model response demonstrates a comprehensive understanding of the product description and effectively suggests multiple relevant categories. It provides clear justifications for each category, referencing specific features like "voice control," "high-fidelity audio," and compatibility with "home automation systems." The "Consistency Check" section further reinforces the categorization by aligning the suggested categories with the product's characteristics. The response is well-structured and logically organized, showcasing effective prompt engineering techniques for product classification. The response accurately categorizes the product across multiple relevant categories, demonstrating a robust handling of variations in product description phrasing. Expand
Metric
Validation Robustness
Score
Reason
The response demonstrates a robust approach to validation. It provides a clear categorization of the smart home speaker into multiple relevant categories (Electronics > Smart Speakers, Home Automation > Voice-Controlled Devices, Audio > High-Fidelity Audio Equipment, Smart Home > Compatible Devices). The justification for each category is well-explained, referencing specific features of the speaker (voice control, high-fidelity audio, compatibility with home automation systems). The "Consistency Check" section further validates the choices by highlighting how each category aligns with the product's characteristics. This comprehensive approach ensures consistent categorization and avoids potential errors, fulfilling the requirements for a Grade 2 evaluation. Expand
Evaluation Results
Evaluation Report
Introduction
Evaluation target plan
[Multi-Step Product Categorization Strategy](/genflows/CSDJzLZDT2YovwjjSL5w/develop/jX8YmxzkTbuoegx_vrjRCw)
Datasets to test this AI model
We've prepared 30 cases from 3 major use cases, generated by LLM Dataset Synthesizer, like:
User Categorization: Users submit product descriptions to get category suggestions
Inventory Management: Automated categorization aids in efficient stock organization
Data Analysis: Accurately categorized data improves market analysis and trend prediction
LLM Judge
We've simulated this AI from the prepared test datasets and analyzed the response by LLM Judges. We evaluated with 3 metrics, which are 3-grade labeling on either "Perfect", "OK" or "Bad".
The LLM Judges used are as follows:
Prompt Effectiveness for Accurate Product Categorization: Prompts consistently and effectively guide accurate product categorization across diverse product types and descriptions, fully aligning with the PRD's objective.
Validation Robustness: Validation methods consistently and reliably ensure categorization quality, fully meeting the PRD's reliability requirements.
Categorization Accuracy: Categorization accuracy fully meets the PRD's requirements for matching user descriptions to the correct product categories.
Evaluation Results
Performance
Categorization Accuracy: The AI achieved a perfect score
54.5% of the time, with the remainder being just OK at 45.5%. This suggests room for improvement in distinguishing between categories, particularly when handling nuanced descriptions.Prompt Effectiveness: With a perfect score achieved
87.9% of the time, the prompts are highly effective, indicating strong prompt design. However, there's a slight gap for enhancement, as 12.1% of the results were only marked as OK.Validation Robustness:
15.2% rated as OK, while 84.8% received a perfect score. The validation process generally holds up well, but further refinement could improve consistency.Overall AI Evaluation by Categorization Metrics
Differentiated Behavior by Use Case
User Categorization and Inventory Management: Both show similar behavior with averaged scores close to
1.8 for most metrics, but Prompt Effectiveness is slightly lower at 1.4, indicating potential struggles with user-specific descriptions.Data Analysis: Scores consistently around
1.9, which is relatively strong but shows a slight dip in Categorization Accuracy, suggesting there might be challenges in handling complex data sets.Uncategorized: Validation Robustness and Categorization Accuracy score
1.8, which implies difficulties in processing atypical descriptions. These areas need further tuning for edge cases.AI Performance by Use Case
Takeaways:
Overall performance is generally strong, especially in prompt construction.
Attention to nuanced categorization and robustness in handling diverse product descriptions can further enhance the AI's effectiveness.
Focusing on user-centric descriptions and complex data analysis may improve consistency across all use cases.
Potential Hallucinations & Common Error Patterns
Inconsistent Categorization:
The AI occasionally misidentifies or inconsistently categorizes products, leading to less precise results. For instance, when given the input
description: "Men's running shoes, made from breathable material, size 10, blue", it appropriately identified Footwear > Running Shoes, but also inserted broader or tangential categories like Sports > Athletic Gear. Similarly, it classified description: "Raspberry." under Food & Beverage > Fruits, but extended to less pertinent categories such as Health & Wellness > Superfoods.Failure to Provide Definitive Output:
The requirement for a structured JSON output was sometimes unmet, evident in the case of
input: "Portable electric mixer, red and white, with a set of beaters and whisk", where although the response provided potential categories, it didn't finalize one definitive category, thus not meeting the PRD specification.Overgeneralization:
The AI often extends classifications to potential but unnecessary categories, diluting relevance. For example,
input: "Bluetooth headphones, noise-cancelling, over-ear, black" was accurately classified under Electronics > Headphones, yet it included minor relevances like Fashion > Accessories > Headphones, which may not be essential for most contexts. This reflects a trend of extrapolating beyond core attributes, risking misalignment with core product identification goals.Conclusions
Is this model production ready?
Almost ready, with the AI achieving a perfect or OK score in categorization accuracy
100% of the time, and demonstrating particularly high efficiency in prompt effectiveness with 87.9% perfect score. However, occasional inconsistencies in validation robustness, rated OK at 15.2%, suggest that the AI might still require slight refinement before full-scale deployment. The AI shows promising potential, but human oversight is crucial initially to ensure it adapts well to unanticipated inputs.Future Improvements
Enhance Categorization for Edge Cases: The AI sometimes misclassifies or generalizes, indicating a need for improved nuance recognition. By incorporating additional training focused on rare descriptive cases or edge scenarios, the AI could better handle atypical inputs without extending into unnecessary categories. Leveraging transferred learning or integrating specific external knowledge sources could aid in refining accuracy for complex or borderline product descriptions.
Refine Validation Rules: Given that the validation robustness has areas marked as OK, it is essential to bolster the validation framework. This involves strengthening the logic to better manage and rectify inconsistencies, particularly in handling the JSON output requirement. Establishing more detailed validation feedback loops could improve reliability in providing a definitive and structured output that fully aligns with PRD specifications.
Integration
How this model is served
The Product Categorization AI Agent is already deployed and can be accessed through the endpoint:
https://tmmt.ly/:id. This endpoint is fully operational and ready for integration into your e-commerce platform or relevant systems for automated product categorization.Integration Example
For the use case of E-commerce Product Categorization via a Python script, you can utilize the API in the following way:
1.Automated Product Categorization with Python
python
import requests
import json
# API endpoint and authentication
url = "https://tmmt.ly/:id"
headers = {
"Authorization": "Bearer YOUR_TEAMMATELY_API_KEY",
"Content-Type": "application/json"
}
# Product description input
product_description = {"description": "This is a red cotton t-shirt, size medium."}
# Sending a POST request to the API
response = requests.post(url, headers=headers, data=json.dumps(product_description))
# Parsing and displaying the response
if response.status_code == 200:
category = response.json().get("category")
print(f"Product Category: {category}")
else:
print(f"Failed to categorize product: {response.status_code}")For the use case of Inventory Management Categorization within a Node.js application, you can employ the following integration:
1.Automated Inventory Management with Node.js
javascript
const axios = require('axios');
// API endpoint and configuration
const url = 'https://tmmt.ly/:id';
const config = {
headers: {
'Authorization': 'Bearer YOUR_TEAMMATELY_API_KEY',
'Content-Type': 'application/json'
}
};
// Product description input
const productDescription = { description: "High-quality blue denim jeans, size 32." };
// Function to categorize product
async function categorizeProduct() {
try {
const response = await axios.post(url, productDescription, config);
console.log("Product Category:", response.data.category);
} catch (error) {
console.error("Error categorizing product:", error.response.status, error.response.data);
}
}
categorizeProduct();Frontend Example
Frontend Example
Next: How to Improve More?
AI Challenges and Considerations
Model Limitations: Ensure you are aware of any current limitations within the AI model. Certain language nuances, domain-specific contexts, or rare data patterns might not be fully captured. It's crucial to document these limitations and keep them in mind during deployment and use.
Intended Use Cases: Clearly identify and communicate the intended use cases for the AI application. This helps prevent the application of the model in scenarios where it may perform poorly or deliver suboptimal results.
Potential Improvements
Comprehensive Evaluation: If not already performed, evaluate the AI model with a broad set of test cases. A diverse dataset can help identify edge cases and performance in real-world scenarios. Teammately Agents can assist in synthesizing test cases and generate tailored LLM Judges to conduct evaluations at scale, providing insightful feedback to refine the model.
Integration with Knowledge Bases: Consider integrating the AI model with external knowledge bases to enhance its information retrieval capabilities. For example, connecting to a domain-specific database or a popular open dataset can significantly improve the contextual accuracy of the outputs.
Optimizing Model Size: Investigate the potential of deploying smaller models to reduce computational costs and latency. Teammately Agents can assist in experimenting with different model architectures while ensuring quality remains consistent. Continuous evaluation by LLM Judges can support this process and help maintain performance standards.
By addressing these areas, you can enhance the effectiveness and efficiency of your AI application, ensuring it meets user expectations and performs optimally in desired scenarios.
Now it's your turn. Tell what AI you want to build.
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