Inaccuracies generated by LLMs can have huge consequences – particularly for telecoms operators and service providers. Reducing the possibility of such mistakes is of critical importance. RAG can lend a helping hand – and also build new levels of protection to support Agentic-based autonomous operations. How? Through internal, curated knowledge bases.
Telecom operators and service providers depend on accuracy. Making the correct decision, with the best information available is critical. There’s nothing new in this, and it applies to many sectors, not only to telecoms – but with the growing adoption and maturity of Generative AI across operational systems and processes, the issue of accuracy has been thrown into sharp relief.
That’s not because AI is inherently inaccurate – far from it. Rather, it’s because even trusted AI can, occasionally, introduce errors. The problem is that it can be hard to spot such errors, which can lead to potentially catastrophic but well-intentioned decisions.
In our industry, this matters, because we must consider a number of factors that combine to mean accuracy is paramount. These include:
Complex, multi-technology, multi-generation, and multi-vendor operational systems
SLA-backed service delivery with little margin for error
Real-time service orchestration and management
Regulatory oversight with the power to impose stringent penalties and fines
Add these to the fact that telecoms networks constitute critical national infrastructure – and the impact of errors becomes of huge significance.
So, what’s to be done? If so-called naïve AI is not sufficient for industrial and safety-critical use cases, how can we reduce the possibility of such errors and deliver better outcomes?
That was the theme of our recent webinar “Agentic RAG – Smarter AI for Network Assurance”. During this live session, we discussed why such errors can emerge – and how we can mitigate them.