API + AI: Building an LLM-Powered System for Automated Product Copy and Short Video Scripts

In e-commerce operations, content creation has long been a core bottleneck for marketing teams. Whether it’s product detail pages or short-form video scripts for platforms like TikTok or Douyin, content production is repetitive yet essential. These assets directly influence consumer conversion and impact brand professionalism and trust.

By integrating e-commerce APIs to obtain structured product data and user review keywords, then leveraging large language models (LLMs), it’s possible to significantly boost both the efficiency and accuracy of content generation. This article provides a practical guide to building a data-driven content pipeline—from data retrieval to prompt design, content generation, and final deployment—for scalable, automated production.

1. Overview of the Goal

This system aims to build an API + LLM-based automated content generation flow, with core capabilities including:

• Retrieving product details and user reviews

• Extracting high-frequency keywords from reviews to construct prompts

• Using LLMs to generate the following content:

  • Product detail page copy (product intro + selling points)

  • Short-form video scripts (structured 60-second pitch for TikTok or Douyin)

The system can be used for mass content production, social media marketing, and influencer operations.

2. Core Data Sources

To enable reusable content workflows, multiple platforms’ structured data and user feedback need to be aggregated. Below are key data sources and sample APIs:

These sources can be accessed via scraping or authorized APIs and provide essential context for prompt construction.

3. Prompt Construction Strategy

High-quality output depends on precise prompt design. To avoid generic or exaggerated content, this system uses a review keyword–driven reverse prompt strategy:

• Prioritize real user high-frequency keywords

• Simulate real usage scenarios and user pain points

• Incorporate seasonality, trends, and competitor comparisons

Example Prompt Logic:

def build_prompt(product_info, hot_comments):
keywords = ", ".join(hot_comments[:5])  # Take top 5 keywords
return f"""You are an expert in e-commerce content writing. Based on the following product details and user reviews, generate a product description suitable for the product detail page.
Product Name: {product_info['title']}
Key Specs: {product_info['attributes']}
Review Keywords: {keywords}
Requirements:
1. Start with a hook to grab user attention
2. Highlight key benefits instead of just listing specs
3. Integrate real user opinions and feedback
4. Use natural, consumer-friendly language (avoid exaggeration)
Please generate a product description of approximately 150–200 words.
"""

4. Implementation: One-Click Product Copy and Video Script Generation

Here is a Python code example for retrieving data and using GPT to generate copy:

import requests
import openai
# Step 1: Fetch product details
def get_product_detail(product_id):
url = "https://luckdata.io/api/lazada-online-api/x3fmgkg9arn3?site=vn&itemId=2396338609"
return requests.get(url, params={"id": product_id}).json()
# Step 2: Extract review keywords
def get_comment_keywords(product_id):
url = "https://luckdata.io/api/tiktok-api/gqJ8UsGWZJ2p"
data = requests.get(url, params={"product_id": product_id}).json()
keywords = extract_hot_keywords(data["comments"])  # Use TF-IDF or TextRank
return keywords
# Step 3: Generate content
def generate_copy(prompt):
openai.api_key = "YOUR_API_KEY"
res = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return res['choices'][0]['message']['content']
# Step 4: Main flow
def main(product_id):
info = get_product_detail(product_id)
keywords = get_comment_keywords(product_id)
prompt = build_prompt(info, keywords)
copy = generate_copy(prompt)
print("Generated Product Copy:\n", copy)

5. Extended Use: Generating TikTok Video Scripts

Short-form video is a high-conversion medium. LLMs can also be used to generate 60-second product scripts with the following structure:

You are a skilled TikTok e-commerce scriptwriter. Please write a short-form script based on the following information:
Structure:
1. First 5 seconds: Eye-catching hook (user pain point or comparison)
2. Middle: Highlight product features and benefits using review keywords
3. End: Strong CTA (call to action) and display of positive feedback
Product Name: XXX
Selling Points: Waterproof, ultra-lightweight, summer-friendly
Review Keywords: Breathable, affordable, good quality, true to size, repeat purchase
Please format your output as either Markdown or JSON for easy deployment.

6. RAG Optimization: Retrieval-Augmented Generation

If you have your own e-commerce knowledge base, you can build a Retrieval-Augmented Generation (RAG) system that enriches LLM prompts with contextual data.

Workflow:

• Vectorize review content, product descriptions, and store ratings using tools like OpenAI Embeddings

• Use vector search tools like Faiss to perform top-k similarity retrieval

• Inject retrieved content into the prompt context

• Send to the LLM for generation

This process enhances the factual grounding of generated content and reduces hallucinations or inaccuracies.

7. Deployment Suggestions: Lightweight Automated Content Platform

To operationalize this process at scale, consider building a lightweight platform:

• Backend: FastAPI or Flask, PostgreSQL or MongoDB for data storage

• Frontend: Streamlit for prototyping or Vue for production use

• Core modules:

  • Bulk product import (CSV or Excel)

  • API integration for auto-fetching keywords

  • Prompt builder and LLM interface

  • Content output storage and batch export

✅ Add versioning and A/B testing modules to evaluate content performance across campaigns.

8. Conclusion: Toward a Data-Driven Content Creation Paradigm

When your content generation starts with robust data and ends with a powerful expression layer like an LLM, you unlock a scalable, efficient, and robust e-commerce content pipeline.

✅ To maximize ROI, integrate with:

• Trending search monitoring and competitor price tracking → automated product selection and pitch script creation

• Real-time keyword monitoring → dynamic ad copy optimization

This paves the way for building a Reactive Content Engine—a system where content is no longer static but in active conversation with live data.

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