RAG for Amazon Sellers and Claude Model Switching | Seller Sessions

Retrieval Augmented Generation (RAG) offers Amazon sellers a powerful way to break data bottlenecks by organizing business knowledge—such as spreadsheets and supplier terms—into searchable, source-backed databases. By chunking data, creating embeddings, and implementing guardrails, sellers can query internal information without the risk of AI hallucinations. Beyond RAG, optimizing AI workflows requires strategic model switching to balance cost and performance. Utilizing tools like OpenRouter allows for the integration of cost-effective models like DeepSeek for routine tasks, preserving higher-tier model usage for complex operations. While AI-driven video production tools like OpenMontage demonstrate significant progress in automating content creation, they still necessitate deep domain expertise to achieve professional results. Ultimately, successful AI implementation relies on building robust, version-controlled systems that prioritize efficiency and data accuracy over mere model experimentation.