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The four biggest challenges in building AI text analysis solutions

With the rise of generative AI, product leaders are frequently tasked with developing solutions to analyze text and generate automated reports, summarizing large amounts of qualitative data that previously required an analyst to spend many hours manually coding.

But simply throwing free text responses into ChatGPT has several problems. Here are some key challenges that you’ll need to consider when building any text analysis solutions.

AI hallucinations

Many of us have witnessed the hilarious mistakes that AI can make, or how it can be fooled into contradicting itself with different prompts.

But when it comes to research, the model you are using must be reporting on data and not simply imitating what it thinks an answer should look like.

That’s why it’s crucial to make sure the AI is being informed by real results. In addition, the prompts should prevent any “creativity” from the language model.

A data “black box”

An analyst needs to be able to provide data to back up their findings when questioned on their reporting.

However, if AI makes an assertion without the ability to trace the specific data points used, analysts won’t be able to provide detailed information when needed. It’s important for product leaders to develop solutions that provide the ability to link outputs to specific data points.

Data security

When using free language models (LLMs) to process data, it’s important to prioritize data security. Submitting data to an LLM often means that the data enters the public domain and is used to train those models.

This can be unacceptable to clients and may even violate data protection laws. It’s crucial to consider these factors when using GPTs.


Finally – and most importantly – GPTs don’t typically generate metrics from data. Simply, it is not what they’ve been designed to do. However, analysts typically can’t rely on a summary without metrics.

For example, check out the below summary of responses to a beer brand survey. While the output is interesting, there are no metrics to back it up.

Ideally, text analysis solutions should provide not only an AI-powered summary but also metrics that allow the analysts to ensure that their reports are accurate and backed up by evidence.

Relative Insight, as a text analysis specialist, has spent more than 10 years developing an advanced NLP parsing engine that tackles the challenges mentioned above while incorporating Generative AI to provide rapid reporting.

Get in touch to learn more about how we can help you build advanced text analysis capabilities!

Let’s build text analysis solutions together