GenAI
Generative AI (GenAI) refers to unsupervised and semi-supervised deep learning algorithms that enable computers to use text, audio, video, images and code to generate new content. Generative AI is powered by foundation models like GPT-4 and stable Diffusion. LLM tech is breaking records in popularity because it’s extremely user-centric. Analyzing, researching, learning, and making decisions are a crucial part of doing work. LLM apps are useful at another approach for learning and researching, but if you solely rely on them their drawbacks are going to keep you behind.
Text is the most advanced domain
Code generation is likely to have a big impact on developer productivity
Images are a more recent phenomenon
Speech synthesis has been around for a while
Video and 3D models are coming up the curve quickly
If you are looking to evaluate GenAI Strategy you can ping us at sivaram@phygitalytics.com
GenAI Zero to One
Introduction to foundation models
Vision models and Applied use cases
AIFilm making, Marketing Tools
Foundational Text models for marketing use cases
LLM for chatbots
LLM + VectorDB use cases
RAG Applications, indexes, search and chunking Strategies
GenAI use cases across domain
GenAI + Vision use case (Real time project walkthru) - Recipe Recommendations
GenAI Recommendations
Spotting False Positives in GenAI Implementation
A demo that works may not be the solution you need. Validate its effectiveness with real-world scenarios?
Do not trust the LLM until your first 100 users are happy with it. User feedback is invaluable.
Do not rely on benchmarks that do not reflect your data. Trust your own metrics.
Do not force-fit an LLM use case to get a promotion. You can choose it only if it genuinely adds value.
Prioritize accuracy first, then reduce costs. Achieving everything at once is unrealistic.
If someone promises a working solution in one month, be cautious. It might be a prototype, not a production-grade solution.
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