The rise of Artificial Intelligence has been the talk of the business world since the emergence of ChatGPT earlier this year.
Now executives around the world find themselves in need of understanding the importance and power of Large Language Models in delivering potentially ground-breaking use cases that can bring greater efficiency and accuracy to mundane tasks.
Natural Language Generation (NLG) enables computers to write a human language text response based on human generated prompts.
What few understand is that there is still a deep flaw in the ChatGPT technology: up to 20-30% of all results have inaccuracies, according to Gartner.
What Gartner have found is that ChatGPT is “susceptible to hallucinations and sometimes provides incorrect answers to prompts. It also reflects the deficiencies of its training corpus, which can lead to biased or inappropriate responses as well as algorithmic bias.”
To better understand this, it’s key to consider how LLMs work: hundreds of billions of pieces of training data are fed into the model, enabling it to learn patterns, associations, and linguistic structures. This massive amount of data allows the model to capture a wide range of language patterns and generate responses based on its learned knowledge.
However, as vast training data can be, the model can only generate responses as reliable as the information it has been exposed to. If it encounters a question or topic that falls outside the training data or knowledge cutoff, responses may be incomplete or inaccurate.
For this reason, and to better understand how best to use LLMs in enterprise environments, Gartner outlined a set of AI Design Patterns and ranked them by difficulty of each implementation.
We are delighted to share that Altilia Intelligent Automation already implements in its platform two of the most complex design patterns:
- LLM with Document Retrieval or Search
- This provides the potential to link LLMs with internal document databases, unlocking key insights from internal data with LLM capabilities
- This provides much more accurate and relevant information, reducing the potential for inaccuracies due to the ability to the use of retrieval.
- Fine-tuning LLM
- The LLM foundation model is fine-tuned using transfer learning with an enterprise’s own documents or particular training dataset, which updates the underlying LLM parameters.
- LLMs can then be customized to specific use cases, providing bespoke results and improved accuracy.
So, while the business and technology world has been getting excited by the emergence of ChatGPT and LLMs, Altilia has already been providing tools to enterprises to leverage these generative AI models to their full potential.
And by doing so, thanks to its model’s fine-tuning capabilities, we are able to overcome the main limitation of a system like OpenAI’s ChatGPT, which is the lack of accuracy of its answers.
For more information on how Altilia Intelligent Automation can help your organization, schedule a free demo here.