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What Is the Strategy of Using Existing Models for GenAI Product Development?
Explore the strategy of using existing models, a method where developers access a foundation model like ChatGPT via an API to build custom GenAI products without the need for training or fine-tuning.
Question
Using an API (Application Programming Interface) to access a company’s flagship model (like one behind ChatGPT) for a custom product is an example of which strategy for building GenAI products?
A. Training a smaller, specialized model.
B. Fine tuning a foundation model.
C. Using existing models.
D. Prompt engineering via no-code tools.
Answer
C. Using existing models.
Explanation
This is described as the “first and easiest” approach, where an organization essentially rents the intelligence of a large, pre-trained model over the internet without needing to develop, train, or even fine-tune the model itself.
This strategy is one of the most common and accessible ways to build GenAI-powered products. It involves leveraging a large, powerful, pre-trained foundation model created by another company (like OpenAI’s GPT models or Google’s Gemini) by accessing it through an Application Programming Interface (API). In this approach, a developer sends a user’s prompt to the model via the API and receives the generated output, which is then integrated into their own application.
This method allows companies to incorporate sophisticated AI capabilities into their products without incurring the immense cost, time, and expertise required to train a foundation model from the ground up. They are essentially “renting” the model’s intelligence on a pay-per-use basis.
Analysis of Other Options
A. Training a smaller, specialized model: This is a completely different, more resource-intensive strategy. It involves gathering a unique dataset and building and training a new model from scratch to perform a specific, narrow task.
B. Fine tuning a foundation model: This is an intermediate strategy. It starts with an existing foundation model but involves an additional training step where the model is further trained on a smaller, proprietary dataset to specialize its knowledge or style for a particular domain. This goes beyond simply using the off-the-shelf model via an API.
D. Prompt engineering via no-code tools: Prompt engineering is the practice of designing effective inputs to guide the model’s output. While it is a crucial skill used when interacting with a model via an API, it is a technique within the strategy, not the overarching product-building strategy itself.
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