Steps to successfully implement generative AI
Posted: Sun Jan 19, 2025 7:16 am
Step 1: Understand the problem and identify potential use cases
Generative AI has billions of applications. But using it for every task makes things more complicated rather than simpler. Problems such as inconsistent results, inaccuracy and data vulnerability are rapidly increasing.
So, carefully choose the problem you want to solve with this technology. Then, list and prioritize the tasks or operations where implementing generative AI significantly impacts efficiency, cost, and scalability.
Pro Tip: If you're new to a generative AI model, we suggest automating kuwait whatsapp number data low-risk tasks like data entry, meeting scheduling, calendar management, etc. first. This minimizes risk while you get familiar with the technology. It also allows you to explore more deployments as you scale.
Step 2: Prototyping Phase
It is time to prototype a generic AI model that effectively addresses the identified problem. There are three main steps in this phase:
#1: Data collection
The first step in creating any AI model is data collection – gathering the data that will be used to train and test the model. This is crucial as it allows the AI model to identify patterns and trends from which it will generate results.
So, start by identifying relevant data sources , which could be social media platforms, search engines, websites, or your own company data. Once you've done this, collect a variety of high-quality structured and unstructured data from them.
Since the sequential and non-sequential data collected is unstructured, you need to provide additional context to improve the overall accuracy and effectiveness of your generative AI model. This is where data labeling comes into play.
**Data labeling involves assigning contextual labels or annotations to data. Popular data labeling techniques include crowdsourcing, active learning, and transfer learning.
Generative AI has billions of applications. But using it for every task makes things more complicated rather than simpler. Problems such as inconsistent results, inaccuracy and data vulnerability are rapidly increasing.
So, carefully choose the problem you want to solve with this technology. Then, list and prioritize the tasks or operations where implementing generative AI significantly impacts efficiency, cost, and scalability.
Pro Tip: If you're new to a generative AI model, we suggest automating kuwait whatsapp number data low-risk tasks like data entry, meeting scheduling, calendar management, etc. first. This minimizes risk while you get familiar with the technology. It also allows you to explore more deployments as you scale.
Step 2: Prototyping Phase
It is time to prototype a generic AI model that effectively addresses the identified problem. There are three main steps in this phase:
#1: Data collection
The first step in creating any AI model is data collection – gathering the data that will be used to train and test the model. This is crucial as it allows the AI model to identify patterns and trends from which it will generate results.
So, start by identifying relevant data sources , which could be social media platforms, search engines, websites, or your own company data. Once you've done this, collect a variety of high-quality structured and unstructured data from them.
Since the sequential and non-sequential data collected is unstructured, you need to provide additional context to improve the overall accuracy and effectiveness of your generative AI model. This is where data labeling comes into play.
**Data labeling involves assigning contextual labels or annotations to data. Popular data labeling techniques include crowdsourcing, active learning, and transfer learning.