GenAI requires a unique approach to development—one that balances business goals with continuous learning. Here's how to structure your GenAI initiatives for success:
The Business-Driven Experimentation Process
Define Success: Start with clear business objectives—whether that's reducing customer service response time by 40%, improving accuracy of bot responses, or increasing sales conversion rates. Document both your quantitative targets and qualitative success criteria.
Hypothesize: Form specific predictions about improving your GenAI solution. Examples: 'Switching to a more advanced model will reduce hallucinations by 40%', 'Using RAG will improve accuracy from 70% to 95%', 'Implementing chain-of-thought prompting will boost reasoning accuracy by 30%'.
Experiment: Run controlled tests across different models:
Test the effectiveness of changes to your prompts
Compare various AI models and vendors to find the best fit
Experiment with different types of input data and formats
Use evaluation frameworks (Evals) to systematically assess performance
Document all variations and their outcomes systematically
Analyze: Evaluate results against your business metrics. Look at both the numbers (like accuracy rates or time saved) and qualitative feedback from users or stakeholders.
Iterate: Use your findings to refine the approach. This might mean adjusting prompts, changing model parameters, or even revising your initial assumptions.
Validate: Before full deployment, test your improved solution with a small user group. Monitor real-world impact and gather feedback before scaling up.
This systematic approach ensures that your GenAI initiatives remain grounded in business objectives while providing a clear framework for improvement and scaling. Each cycle of experimentation brings you closer to optimal business results.
Learn more about how to experiment with Arato.