At this point, you’re probably pretty familiar with the AI hype out there. You’ve likely read that GenAI (like DALL-E or ChatGPT) is great for generating both visual and text-based content, and AI overall can be good for identifying patterns, particularly in large data sets, and providing recommendations (to a certain degree).

But you may also be familiar with the myriad ways GenAI has gone sideways in recent months (ex: Intuit’s AI tax guidance debacle, New York City’s law-breaking chatbot, the Air Canada lawsuit, and so many more). That doesn’t mean you need to stop experimenting with it, of course. But it does mean that the folks warning about it not being ready quite yet have some valid points worth listening to. 

Having built several AI solutions, including a recent GenAI LLM (large language model) solution, here’s some unsolicited advice to consider when leveraging a GenAI LLM. 

Don’t use GenAI for situations where you need a defined answer.

As evidenced in all the examples above, GenAI chatbots will – and often do – make information up. (These are called hallucinations within the industry, and it’s a big obstacle facing LLM creators.) The thing is, this is a feature, not a bug. Creating unique, natural-sounding sentences is precisely what this technology is intended to do and fighting against it is – at least with the current technology – pointless. 

There are some technical guardrails that can be set up (like pointing the system to first pull from specific piles of data, and crafting some back-end prompts to tell it not to make things up) yet still, eventually, our bot friends will find their way to inventing an answer that sounds reasonable but is not, in fact, accurate. That is what they are meant to do. 

In situations where you need defined, reliable pathways, you’re better off creating a hardcoded (read: not GenAI) conversation pathway that allows for more freeform conversation from the user while responding with precise information. (For the technically-minded, we took a hybrid format of GenAI + NLU for our latest automation and found it quite useful for ensuring that something like following a company-specific process for resetting a password was accurate and efficient – and importantly, in that use case, also more secure.)

Know thy data—and ensure it’s right.

I know it’s been said a million times over but a pile of inaccurate, poorly-written data will provide inaccurate, poorly-written responses. GenAI cannot magically update your data to be clean and accurate – it can, over time, generate new information based on existing information and its style (which should still be checked for accuracy) but asking it to provide correct information when it’s hunting for the answer through incorrect information is an impossible task. It cannot decipher what is “right” or “wrong” – only what it gets trained to understand is right and wrong. 

It’s important then to know what the data that you’re starting with looks like and do your best to ensure it’s quality data – accurate, standardized, understandable, etc. Because barring time to properly train the data (which is a serious time commitment but well worth it for anyone wanting proprietary or custom answers), starting with a clean data set is your best bet. 

Bring the experts in early.

When people have been experimenting with the technology and potential solution for a while, there is a pressure to “get it done already” by the time the experts roll in that doesn’t allow for the necessary exploration and guardrail-setting that needs to happen, particularly in an enterprise setting where there are plenty of Legal, Compliance, Security and even Marketing hurdles to clear. 

From both personal and collected experience, it’s worth noting that often the initial in-house experimentation focuses on the technical aspects without user experience considerations, or even why GenAI might – or might not – be the right solution here.  That’s going to take a little time. So it’s worth bringing in design and/or research experts, whether in-house or consultants, alongside the initial technical exploration to do some UX discovery and help the entire sussing-out process happen in tandem with the technical exploration. This can provide a clear picture of the business case for pursuing this particular solution. 

To help out, the Grand Studio team created a free, human-centered AI framework for an ideal AI design & implementation process.

Interested in knowing how to start a GenAI project of your own? Drop us a line!