AI is not a replacement for engineers, but it can be very useful to product managers testing hypotheses and product ideas.
If you think in terms of the JBTD framework, Deep Search products can save you a ton of time and effort in finding new product and market opportunities.
We were about to shut down a machine learning project. Then we decided to take another look at the data...
High-quality data, effective preprocessing, and model optimization are essential for successful implementation of generative AI applications.
Some hard-earned lessons from the trenches shipping machine learning products.
Think of business goals and interventions before determining your ML metrics.
Start with the low-hanging fruit to reduce friction and build trust in your organization in adopting machine learning across operations.
Standard retrieval can only get you so far. Alignment, contextual retrieval, and reranking can improve your RAG pipeline considerably.
To systematically analyze and fix LLM errors, think of the process in terms of classic ML error analysis.
Experiment with all kinds of generative AI and LLM tools. You'll find out where the industry is headed and how you can prepare for it.