SimplicityDX is a technology company that specializes in building creator storefronts for brands with established creator programs. The platform leverages generative AI for social media to analyze organic creator posts primarily from Instagram and automatically identify promoted products and collections.
Problem: scaling accuracy with generative AI for social media
As creator-led commerce scaled, SimplicityDX relied on generative AI for social media caption analysis using Llama 3 on Amazon Bedrock, followed by fuzzy search against product catalogs. However, creator content introduced unique challenges such as slang, misspellings, emojis, hashtag spam, missing descriptors, and inconsistent product naming.
These issues limited the effectiveness of generative AI for social media, resulting in 74% perfect match accuracy and 92% accuracy when allowing one missing product per post below business expectations. To support reliable creator storefronts, SimplicityDX needed to significantly improve accuracy while continuing to use generative AI for social media at scale.
Solution: enhancing generative AI for social media through prompt engineering
GoML delivered a targeted POC focused on enhancing generative AI for social media content analysis through advanced prompt engineering. The solution leveraged GoML’s AI Content Generation Boilerplate, optimized for structured extraction from informal, unstructured text.
Instead of changing infrastructure, GoML redesigned the LLM prompt framework with clearer instructions, domain-aware examples, and contextual reasoning enabling generative AI for social media to better interpret real-world creator captions.
Prompt enhancement for generative AI for social media
The enhanced prompt framework introduced structured guidance tailored specifically for generative AI for social media analysis:
- Actionable extraction rules for product and brand identification
- Contextual usage cues such as wearing, using, or in my bag
- Category-specific examples for different product types
- Handling of misspellings, slang, synonyms, and product variants
- Improved detection of multiple products and categories within a single post
- Domain-specific knowledge aligned with creator commerce
These improvements allowed generative AI for social media to reason more accurately over noisy, informal content.
Testing and validation of generative AI for social media
The enhanced prompts were tested against an existing dataset of 100 labeled social media captions, including known failure cases.
- Prompt performance was evaluated across Claude Sonnet 3.5, 3.7, and 4, Amazon Nova Models, LLAMA Models on Amazon Bedrock
- Integration testing was performed with the existing fuzzy and trigram search algorithms
- Accuracy improvements were achieved without impacting system performance
Documentation was created to support ongoing optimization of generative AI for social media workflows.
Impact
- ~22% improvement in perfect match accuracy, establishing a clear path toward 90%+ accuracy using generative AI for social media
- 30–40% reduction in extraction errors caused by slang, emojis, misspellings, and inconsistent product naming
- 25% improvement in reliability of creator storefront mappings, reducing incorrect or missing product links
About
Before Gen AI and after Gen AI
“By refining prompt engineering rather than changing infrastructure, we helped SimplicityDX significantly improve the accuracy of AI-powered social media content analysis while maintaining system performance and scalability.”
Prashanna Rao, Head of Engineering, GoML
Key takeaways for commerce and creator platforms
Common challenges
- Social media content is noisy and inconsistent
- Traditional extraction approaches struggle with slang and context
- Architectural changes increase cost and risk
Practical guidance
- Invest in domain-specific prompt engineering
- Use contextual cues to guide LLM reasoning
- Improve accuracy without altering production systems
Ready to enhance your AI-powered content analysis?
Partner with GoML using AI-Matic, our Gen AI adoption framework with 6 proven AI boilerplates, to accelerate the development and optimization of AI-powered social media content analysis systems designed for accuracy, scalability, and real-world creator commerce use cases.
