AI-Powered IoT Data Analytics

With the ongoing expansion of the Internet of Things (IoT), the data generated is vast and continually growing. Hence, it is crucial to make sense of this data to gain meaningful insights that drive smarter decisions.

Pre-trained large language models (LLMs), such as GPT-4, have revolutionized the field of natural language processing (NLP), by opening new ways to extract data-driven insights. Such models, however, only have knowledge about the data they were being trained on. Teaching an LLM knowledge by fine-tuning it with new data is a complex task.

Therefore, alternative approaches have been developed in which the knowledge required to answer a user prompt is no longer derived solely from the LLM, but rather from a connected external knowledge base, such as a database. This approach is also referred to as Retrieval Augmented Generation (RAG).

This additional context can be retrieved by letting the LLM generate a query to be run against the database. For this, the LLM needs to be aware of the data model and available functions in the database. The query result is then passed as additional knowledge to the LLM to interpret and summarize it in the sense of the original user prompt.

This approach, however, also comes with challenges. For instance, it does not scale as too much information can overload the LLM, leading to hallucinations and information being overlooked. We succeeded in overcoming these challenges and even integrating visualizations into the answer making the insights more accessible and actionable.

Is your organization ready to embrace the power of AI to unlock the full potential of your IoT data, driving innovation and competitive advantage?