As many companies have found out the hard way – it's not as simple as just plugging ChatGPT or Llama 3 into your systems or processes. We also need to consider whether ‘traditional’ machine learning or well thought-out UX will do the trick. Or perhaps how we combine all three.
Like any tech implementation, defining a problem statement is critical, as is assessing whether the tech you intend to use is actually going to solve the problem. We have seen many organisations excitedly implementing popular solutions only to rue the haste of their decision further down the line.
In situations such as the one we find ourselves in now, the confluence of service design and data science capabilities come to the fore - skills we have been honing and mastering since 2016. I cannot stress this enough – it is crucial to understanding brand and technology landscapes before delving into the intricacies of LLMs and Generative AI. In fact, it’s so critical that we won’t start development on any such projects until we’re satisfied that these factors have been considered. Only then can we, with a clear conscience, begin to build a GenAI solution.
Once that hurdle has been successfully negotiated, one needs to understand how LLMs function in the Gen AI domain – that is the key to unlocking their potential. In order to simplify the understanding of how a LLM needs to be structured for an implementation, we need to deconstruct its building blocks:
- A language model generally functions for a single language - English, French, Sotho, Arabic, Swahili etcetera. Unfortunately, not all languages are the same - the vast majority are under-resourced and therefore require specialist training in order to facilitate human/machine interaction. A well-resourced language typically has to process at least 10,000 books in order for the corpus of data to be useful. As Helm we have designed models that work very well under the low-resource constraints. The language model naturally has access to data in order to make it relevant to the application - this is the basis of LLMs such as GPT4, Claude, Gemini, Llama 3 etc - we have integrated these models where necessary to deliver some of our applications.
- Once you have a baseline language model, it then needs to be tuned for domain-specific interactions. For example, the word ‘balance’ has different connotations within the finance realm as opposed to telecommunications – one provides a view on your outstanding credit card or investment account balance, whereas a telco would be referring to data bundles, minutes and so on. This requires training by highly educated data scientists.
- The training does not stop there though. Each business has specific terminology that needs to be aligned with to accommodate FAQs, rules, product info and the like.
- In our experience though, we find it advantageous to explicitly leverage product-specific language with our models to really ensure that the interactions are meaningful and the customer experience is as expected. Take the word balance again - for an investment account it has a positive meaning, but the same can’t be said for a home loan or other forms of credit.
These are the tips of many, many icebergs.
This level of sophistication requires not only skill, but a platform robust enough to manage any mode of input and marry them with the correct response. This requires language detection, sentiment analysis, intent mapping, integrations and much more. Doing the same for other languages like Zulu or Afrikaans is therefore not just a simple exercise of translation. Further work needs to be done in order to leverage the initial investment and effort.
The above training of these LLMs needs to cater for text-based interactions as well as factor in alternative modalities – voice, vision or even virtual reality.
It is a big, big undertaking, and you can now see why it’s not as simple as plugging the likes of ChatGPT into your brand platforms.
We have just taken a new product to market called Helm Gen, which makes use of a pivotal advancement in the realm of LLMs called Retrieval-Augmented Generation (RAG). Once we have unpacked the problem, investigated and addressed all of the considerations above, Helm Gen uses RAG to provide a Generative AI solution that operates within the bounds of brand content, while limiting ‘hallucinations’.
The reason I mention this now is that the considerations I have raised here are not intended to dissuade anyone from using these GenAI solutions – all we are trying to do is ensure that businesses understand the investment required to truly realise the benefits of Generative AI for business.
