In all my GenAI product-building efforts, these questions consistently arise across various tasks: Data, ETL, Marketing, NER, Fashion, Design, and ESG.
- Choosing between vision models and text descriptions: When should you use vision models versus text descriptions? For the same task, OCR provides a certain level of accuracy, multimodal approaches yield different accuracy levels, and benchmarking takes time. Should a hybrid approach be considered?
- Improving model accuracy: How do you balance the use of low-cost models versus pursuing high accuracy? What are effective strategies for building products while minimizing costs?
- Catching critical hallucinations before production: How can you effectively address cases where a model misclassifies metrics in its interpretation?
- Maintaining transparent communication about AI limitations: How do you handle situations when founders ask, "Company X does this—why can't we?" especially when you lack insight into their models, architecture, or talent?
- Building trust through transparency: How can you reinforce that being open about AI limitations builds long-term trust? Developing production-grade applications requires considerable time and effort.
- Encouraging models to admit uncertainty: What are innovative ways to make models reason through their uncertainty, validate it, and improve reliability using multiple methods or ensemble approaches?
AI Advisory involves a combination of solution evaluation, in-depth research, continuous learning, hands-on coding, and assisting others in troubleshooting and resolving their issues.
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