Updated: Jul 17
LLMs are entering a critical phase as an increasing number of companies move towards integrating them into real-life business applications. While GPT-style models perform admirably initially, developing practical solutions in real-world scenarios remains complex.
Is prompt engineering alone sufficient to achieve the desired accuracy? Are you open to sharing your data with a major vendor? Under what circumstances does training your own LLM lead to more stable outputs?
In this webinar, Gad Benram, founder of TensorOps, will share his experience in building real-life applications for LLMs and showcase three reasons why training an LLM is advantageous, as well as three reasons why relying on out-of-the-box solutions may be preferable.