What is required to Turn Data Into AI Products ? - My perspectives
- Experimentation on Different levels of data / Summary / Key-Values / Multimodal
- Data curation / Setting up Relationships
- Adding domain knowledge
The main reasons cited are:
- Poor data quality
- Inadequate risk controls
- Escalating costs
- Unclear business value
So, the lesson is, my perspectives are:
- The model is not lift-and-shift — customize it for your needs. A demo that works may not be the solution you need.
- Build your data and benchmark with your data. Do not rely on benchmarks that do not reflect your data.
- Have an LLM cybersecurity, data governance, and guardrails in place. Do not trust the LLM until your first 100 users are happy with it.
- Escalating costs—first, get the accuracy right, then reduce costs. You cannot achieve everything at once.
- Unclear business value — Do not force-fit LLM use case to get a promotion. Only opt for it if it genuinely adds value.
- If someone promises a working solution in 1 month remember it can be selling a prototype, not a production-grade solution
Keep Exploring!!!
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