9 August 2023, that’s the date I am writing this article on and I am sure most of you know what chatGPT is, but do you really understand how it works, the implications it can have and what the future could hold regarding the way we interact with it?
ChatGPT is based on the foundational model: GPT.
This model have evolved in the last years based on two principles:
This two components have a direct influence on the quality of the outputs given by the model.
But bigger datasets and more parameters does not mean a better model, the whole magic is to find the sweet spot between both, it’s from that optimisation that rise the magic.
Currently GPT is being used in all industries and the realm of applicability is limitless, but the results are still lacking for the following reasons:
The lack of personalization impacts directly the quality of the outputs and thus the perceived value of the benefits people could get from GPT.
The company behind GPT (OpenAI) understood this and the strategy that rose form that is amazing: giving access to the API to developers letting them optimize the model based on their datasets.
Fine-tuning in the Ai ecosystem is the process through which you can adapt a foundational model to your dataset and specific inquiry.
That’s how the concept of personalized GPT saw the light of the day. In the realm of AI context is the most precious asset:
Personalized GPT allows companies to:
That’s just the tip of the iceberg, as you increase the number of interactions with your personalized GPT you increase the datasets of right interactions. This is how incremental improvements takes place and you get a realistic, efficient and personalized GPT version.
As explained before, the way an AI model is trained is key to it’s success. But the more intentional the training dataset is the better quality you’ll get that’s why personalized GPT model allow their creators to improve exponentially.
This is the best way to get a correct amount of outputs and reduce the error rates.
Let’s have a look at real world applications of Personalized GPT:
Keeper tax:
They personalized GPT-3 for tax write-offs and filing for freelancers. Niche industry in terms of persona but a clear identified skill needed for that.
Viable:
They personalized GPT-3 to transform customer feedback into natural language reports for product teams. Again the persona is niche but the skill is clear and narrowed to one simple action.
Sana Labs:
They personalized GPT-3 for learning experiences, again the skill is clear and the persona for whom it’s done clearer.
These 3 examples shows perfectly the importance of intentional personalization and narrowing down the experience for the user of the new model.
As you understood so far, Personalized GPT is mainly for company use cases in which the reliability and consistency of the outputs are primordial in deciding wether or not to adopt a technology.
When the money you make is based on the output your model gives, tailoring the realm of applicability of your GPT model is a priority even if setting it up will cost you a lot of money.
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