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Generative AI is a type of artificial intelligence that generates new and unique outputs by learning patterns and rules from vast amounts of data.
To determine if this technology is the right tool for your business needs, we recommend evaluating every use case with two factors in mind.
How natural or realistic the output needs to sound
The level of precision required in every output
Example 1 - Writing a blog (High fluency, low accuracy)
When it comes to creator and productivity applications like blog writing, LLMs or even general applications like ChatGPT or Bard AI can be a great fit because the creator is required to create a large output of text from an idea, and the precision of the generated output is subjective.
Example 2 - Provide a consistent result based on a formula (Low fluency, high accuracy)
When it comes to queries that require an objective response, where the answer needs to be the same every time, Gen AI is not a good match. Although the responses sound confident, they could be more reliable.
Example 3 - Writing a business memo (High fluency, high accuracy)
When mixing subjective and objective information, Gen AI can be a good co-pilot, but it cannot be trusted blindly. The creator must review and edit the output before publishing or distributing the results.
If your use case is not clear-cut, another way to identify if Generative AI is the right tool for you is to think about the intended output. Gen AI is fantastic for the following use cases:
Condense lengthy text into concise versions while retaining vital information.
Organize and classify text or data according to pre-established criteria or categories.
Find and extract specific information from unstructured text.
Produce unique freeform content based on preset templates or formats.
Create new content in a different language while preserving its meaning and context based on a given prompt.
Identify the emotion or feeling conveyed in a text, such as positive, negative or neutral.
Data privacy is a valuable asset that introduces a heightened set of risky challenges when engaging with ML and AI services. For instance, if you have utilized the free, public edition of ChatGPT, you are explicitly entrusting your data to OpenAI, who can employ it to train their models.
While the outputs created by these models are indeed impressive, it is important to conduct a comprehensive review and verification of the results. This becomes especially crucial when accuracy is a key requirement. Trusting AI-generated information without implementing a proper review mechanism can yield unintended implications for your business if the output proves to be inaccurate.
Selecting the right model for the task at hand is a pivotal responsibility when harnessing the power of these technologies. The realm of artificial intelligence is vast, and delving into its possibilities can present challenges due to the diverse outcomes produced by various methods and models. We collaborate closely with you to deeply understand your problem space, ensuring that the chosen tools align seamlessly with your project objectives.
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