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NLP vs GenAI comparison has risen in relevance with organizations wanting to use the best of two AI worlds to leverage sentiment analysis. But NLP and GenAI work on different mechanisms and support distinct purposes.
Sample this one – An apparel retail company wanted to build sentiment analysis to understand customer behavior, keep tab on emerging trends and stay ahead of competitors. It boiled down to weighing NLP vs GenAI and leveraging the tech component that would work to its advantage.
The NLP vs GenAI for sentiment analysis empowers organizations to understand strengths of NLP and GenAI and the optimal use cases supported by the technologies, guiding them to leverage the best-fit technology.
Choosing NLP or GenAI?
Here’s a snapshot of scenarios where one approach could be preferred over the other.
GenAI vs NLP in Real World
A logistics company wanted to perform sentiment analysis on customer reviews for their last mile delivery services. The crux of the situation was to go between GenAI and NLP to productionize sentiment analysis.
In a situation where an organization wants to choose between GenAI and NLP, here’s
our comparative analysis that hinges on six key aspects: –
- Accuracy
- Time to market
- Cost to Build
- Operational Costs (Infrastructure)
- Cost to Maintain (Post-production)
- Security
In this scenario, the traditional NLP approach leverages Azure Machine Learning Studio, while the Generative AI approach employs Azure OpenAI Studio.
-
Accuracy
With NLP, baseline accuracy varied based on ongoing model updates. Customization improved accuracy, although it required continuous effort. Traditional NLP models faced challenges with understanding context, sarcasm, and complex sentiment nuances, necessitating frequent model retraining to maintain performance.
With GenAI, LLM delivered high accuracy successfully comprehending and processing a wide range of sentiments in diverse reviews. High accuracy could be maintained even with increasing data flow, and the LLM approach proved superior in handling nuances, sarcasm, and context in customer reviews, enhancing reliability over time without the need for retraining.
-
Time to Market
NLP approach required a time-consuming initial setup due to manual feature engineering, model selection, and tuning. However, the integration of pre-built components, pre-trained models, and drag-and-drop functionality of Azure Machine Learning streamlined the data input and development process. Training and testing time could be reduced while process had to be customized for each use case. The timeline for building a solution was estimated at 8 to 10 weeks.LLM approach gave way for faster development supported by pre-trained OpenAI models that could be leveraged for sentiment analysis with minimal configuration. This approach championed faster setup, easy automation, and seamless API integration. Although fine-tuning consumed additional time, it was significantly quicker than NLP model training. The timeline for building this solution was estimated at 4 to 5 weeks
-
Cost to Build
For the NLP approach, cost included setting up data pipelines, feature engineering, and model development. These expenses were generally higher due to the Azure ML Studio components required for storage, compute, and model deployment. Moreover, this approach demanded more resources and man-hours
The primary cost in the LLM approach was associated with Azure OpenAI API access and varied based on usage (i.e., the number of tokens processed). The initial development phase involved higher costs due to intensive API usage though there were no training costs. Over time, the overall costs depended on the volume of queries processed, with this approach requiring fewer resources and lesser man-hours.
-
Infrastructure Costs
The NLP approach required ongoing maintenance of infrastructure for processing and storage. As data volume increases, compute and storage requirements scale up, thereby increasing costs.
The LLM approach incurred no direct infrastructure costs since Azure managed the backend for the API. Operational costs were driven by the frequency and volume of API calls made to process the reviews. The solution is fully scalable on demand with data volume, but this directly increases API costs.
-
Post-Production Support
Ensuring accuracy in the NLP approach demands periodic model training and tuning in NLP approach. The automated pipeline requires regular monitoring and maintenance to address challenges such as data format changes or scaling issues. These requirements contribute to ongoing costs for maintaining the Azure infrastructure, including compute instances, storage account.
In contrast, the LLM-approach facilitates minimal maintenance costs since OpenAI Studio manages and updates the model. This phase primarily focuses on monitoring API usage and handling the business logic that interacts with the LLM. Ongoing costs are tied directly to API consumption – the more the API usage, the higher the costs incurred.
-
Security
With the traditional approach, all data processing occurred within a controlled Azure environment, security concerns were manageable and aligned with the organization’s policies. Compliance with data handling regulations was easier to maintain since the data was retained in the controlled environment, providing an additional layer of protection.
In the LLM approach, reviews were processed securely via the Azure OpenAI API (a service hosted by Microsoft). Azure OpenAI Studio implements secure data handling protocols, ensuring that data is processed transiently, not stored or used to train the model. The data submitted through Azure OpenAI is isolated from other customers. This data is not accessible to others and is fully compliant with stringent data protection regulations (such as GDPR). Microsoft claims that OpenAI does not use customer data to improve the model, providing a secure and compliant environment for processing sensitive business information.
NLP vs GenAI – Cost Comparison
For this scenario, cost comparison based on the above six pointers resulted in:
2x spending for NLP approach as compared to the GenAI driven LLM
The components that went into cost calculation are captured in the tables below.
Using NLP or GenAI?
The choice between NLP or LLMs ultimately depends on the use case and the significance of the solution.
- NLP is best suited for well-defined, structured tasks with specific rules, feature engineering, and small-scale projects as compared to LLMs.
- LLMs are ideal for more complex language understanding, creativity, and handling unstructured data or open-ended queries. LLMs excel at generating human-like text and handling nuanced conversations and creative content.
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