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Blog
04 June 2026
Author
How can telcos reduce AI hallucinations? Retrieval-Augmented Generation enhances accuracy with contextual data, enabling trusted, real-time network operations.
Blog
04 June, 2026
Author
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.
AI. Accurate to a Fault. Sometimes.
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:
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Complex, multi-technology, multi-generation, and multi-vendor operational systems
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SLA-backed service delivery with little margin for error
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Real-time service orchestration and management
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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.
How Do LLMs Deliver Inaccurate Answers?
There are two main reasons. First, LLMs typically have a cut-off date, so they can only contain knowledge up to a given moment in time. After which, they are essentially frozen – until the next update.
Second, models have inherent confidence. So, they can provide information in a convincing manner, which is believable to humans – the phenomenon of hallucinating.
There’s another factor, too. LLMs rely on public data and may not have access to specific, contextual information that is important to your business. Every operator has a unique mix of systems, vendor solutions, integrations and more. Even if operational principles are common, the OSS, BSS and connectivity fabric will be unique.
As a result, LLMs that have only access to public information cannot apply contextual knowledge to the outputs they generate or make answers specific to your situation and protocols. Consequently, errors can occur – and while most will be inconsequential, some could have a profound negative impact on service performance and operations.
The RAG-enabled knowledge store
How do we solve this problem? Retraining the LLM is an option – but that’s costly and takes time. While it may push the knowledge forward, that same knowledge will also be left behind quite quickly by events. That’s not helpful when you are running a network and things change all the time – even daily.
Another answer is to leverage RAG – Retrieval-Augmented Generation. RAG is a well-established principle but is now attracting renewed attention, because it has the potential to blend private and contextual knowledge, in a controlled manner, with the trained data held in an LLM.
In practice, this means that your internal knowledge – documents, processes, specifications, user manuals and more – can be made accessible to and then used by your LLMs when you make queries via its prompt. This contextual information helps LLMs give you better, more accurate answers.
Towards a Trusted RAG Architecture
To accomplish this, we need to build an architecture that enables new information from this knowledge base to be retrieved when questions are put to the LLM. The knowledge base effectively becomes part of the query workflow and is available to the LLM, but not part of it. This means an extra second or two in response times, but that sacrifice is more than compensated for by the enhanced accuracy.
And, when the answer comes, it will also be supported by citations that show you how and from where it was derived. Better still, you can continually update your knowledge base with dynamic data that reflects the current state of the art – so it’s always fresh and relevant.
Applying RAG architecture in practice
The webinar showcased just such an architecture and how it could be realized in practice. That means a model in which we have a RAG backend for the LLM that allows us to load and process the documents we need for our internal knowledge base.
In essence, this is our catalogue – to which we can add through manual or automated processes the information that we need to augment LLM queries. The data supplied is converted to chunks that can then be mapped and categorized in a database – ready for retrieval.
Alongside the RAG backend, we need an augmentation layer in which these chunks, organized in a manner accessible to the chosen LLM. In turn, the LLM can then retrieve the relevant information in response to prompt-based questions, taking into account user intent.
The chunks represent private knowledge that is contextual to your business and operations, so they answers provided are refined based on this – referenced – output, providing the enhanced accuracy we seek.
Agentic RAG – Towards Governance and Control
Enhancing accuracy is a necessary step to enhance the trustworthiness of LLMs. RAG provides a means to achieve this. But as we also move towards Agentic-based, autonomous operations, it also provides a means to support the extended governance and oversight we will need to safeguard networks and critical services.
Agentic AI operations, are of course, likely to drive the next wave of automation in telecoms networks. Agentic-enabled processes will be able to exchange information and to execute decisions based on the information available.
Once again, we face the risk that hallucinations could lead to inaccuracy – with the additional risk that the inaccuracies could result in erroneous actions that could disrupt the network if left unchecked.
The addition of RAG provides a layer of contextualized governance that can reduce the possibility of such errors. Just as it enhances information retrieval, so too can it provide greater accuracy to other agents that need the same information.
Agents must not exceed their authority
However, here, we can also limit the extent to which different RAG agents are permitted access to different chunks of data. We can retain user rights and permission policies that are applicable to people and implement the same oversight and safeguards for autonomous systems.
In other words, we can implement strict governance controls that mean agents do not exceed their authority – and ensure human interventions for decisions that could have critical impact. Agents cannot simply have unfettered access to information or the ability to make any decision.
In the context of telecoms networks, RAG can do much to provide the necessary context to refine and enhance information retrieval by LLMs. But it can also support Agentic deployments, building a layer of protection into the automation fabric that is subject to the same controls as are applicable to human operatives.
We’re taking the first steps along this pathway, but it’s already clear that the established principles of RAG can do much to support operational safety concerns.
Watch the complete webinar on-demand to explore our RAG innovations in more detail.
