Objectives
After completing this course, the learner will be able to:
■ Define Retrieval Augmented Generation (RAG)
■ List RAG benefits and limitations
■ Identify scenarios where RAG brings value in telecom networks
■ Sketch RAG architecture and process
■ List considerations for RAG
■ Explain tokenization and retrieval criteria
■ Apply LangChain and prompt chaining in RAG
Outline
1. Getting Started with RAG for Telecom
1.1 Introduction to RAG
1.2 Augmenting an LLM with New Data and Fine Tuning
1.3 Augmenting a Large Language Model Using RAG
1.4 Exploring RAG in Telecom Networks
2. Applying RAG to Telecom Networks
2.1 RAG Applications in the Telecom Space
2.2 The Power of RAG in Telecom
2.3 Use Case: Network Performance Monitoring
3. RAG Systems
3.1 RAG Architecture
3.2 RAG 2.0
3.3 Use Case: Network Outage Diagnosis with RAG 2.0
4. Intelligent Workflows with RAG
4.1 Vector Database Operations
4.2 LangChain and Prompt Chaining
5. Conclusion
5.1 Summary