Investing heavily in GenAI for internal engineering teams can be truly transformative, but it also presents certain risks, especially when teams are tasked with making critical design decisions based on AI-generated output.
One key concern is the risk of hallucinations. GenAI models, while impressive, are known to sometimes generate inaccurate or fabricated information, especially when pushed beyond their training data. For engineering teams making crucial design decisions, a hallucinated recommendation or misinterpreted data could lead to costly mistakes or delays. Relying solely on LLMs without rigorous verification mechanisms can compromise the quality and reliability of the outputs.
Additionally, there is the issue of data security. Many companies use external GenAI services that require sending sensitive internal data to cloud-based LLMs. For organizations that deal with proprietary information, this opens up potential risks of data breaches or leaks. Regulatory concerns also arise when transferring data outside company firewalls, particularly in industries that are heavily regulated, such as Aerospace and Defense.
Furthermore, LLMs come with a significant cost factor. Sending large volumes of data to LLMs for processing, especially when the task is not well-suited to such models, can lead to inefficiencies and mounting costs. In the long run, this can be financially unsustainable, particularly for companies looking to scale AI applications within their engineering teams.
The Value of Using Proven NLP Search Technology Before Leveraging LLMs
A more efficient approach involves using a proven natural language processing (NLP) search technology, such as Goldfire by Accuris, as the initial step. By having an NLP system perform the first pass of searching through data, companies can ensure that the most relevant and accurate information is retrieved. This approach mitigates the risk of hallucinations, as the LLM only needs to refine or process verified results, rather than generating output from scratch. Another benefit unique to Goldfire is their focus on technical data. Most NLP providers take a more general “one size fits all” approach.
Moreover, performing this search behind the firewall maintains the security of data. Companies can process their sensitive information within their own infrastructure, ensuring compliance with data privacy regulations while also safeguarding proprietary designs or trade secrets. Keeping the search phase on-premises ensures the highest level of data control, while only leveraging LLMs for tasks that genuinely benefit from their advanced processing capabilities.
This hybrid approach is also more cost-effective. By only sending select, relevant data to the LLM, companies can significantly reduce the computational load and, consequently, the costs associated with processing large amounts of data. This ensures that LLMs are used efficiently, reserving their power for tasks where generative models truly add value.
While LLMs and GenAI offer exciting opportunities for engineering teams, over-reliance on them can lead to pitfalls like hallucinations, security vulnerabilities, and high costs. Using a proven NLP search technology first, before leveraging LLMs for post-processing, helps organizations maintain accuracy, enhance security, and optimize costs—all while ensuring the critical decisions made by engineering teams are based on trustworthy data.
For more information on Goldfire by Accuris, visit the Goldfire page on our website.