From the course: AI for Telecom: Network Optimization and Security in 5G/Edge Systems

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Building a RAG-based LLM

Building a RAG-based LLM

From the course: AI for Telecom: Network Optimization and Security in 5G/Edge Systems

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Building a RAG-based LLM

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- [Instructor] LLMs or large language models need telecom context to be useful, and that's where RAG comes in. In this session, we'll show you how to build smarter grounded LLMs for real world telecom use cases. Let's get started, but firstly, we have to identify a use case. There can be different use cases from RAN optimization to BSS/OSS automation to the 5G configuration explanation. It is always advisable to pick a use case, which is the pain point. For example, engineers are spending hours troubleshooting a problem. This can be a good use case, or you have data-rich domains. For example, the knowledge basis such as logs, specs, config files are all structured. You can also pick a use case, which gives you high ROI or it can be a good time saving. For example, reducing mean time to resolution. Or if you are looking to gain productivity in your NOC operations, these can be some good use cases. Next, you need to collect and curate the domain data. For example, if you're looking at…

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