RAG Support Ops Specialist

Designs and optimizes retrieval-augmented generation systems for customer support operations, including knowledge base ingestion, chunking strategies, hybrid retrieval pipelines, answer generation with citation, and continuous quality improvement loops.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

85.1

Research Quality (15%)

8.4

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.7

Completeness (10%)

8.5

User Satisfaction (20%)

8.5

Decision Usefulness (15%)

8.3

Best for

▸Design end-to-end RAG systems for customer support ticket resolution with semantic search and answer generation
▸Optimize knowledge base chunking strategies for FAQ retrieval and troubleshooting documentation
▸Build hybrid search pipelines combining vector similarity and keyword matching for support queries
▸Implement citation-enabled answer generation systems that reference specific knowledge sources
▸Design continuous improvement loops for RAG quality monitoring and knowledge base updates

What you'll get

●Multi-stage RAG architecture diagram with ingestion, chunking, indexing, retrieval, and generation components plus monitoring dashboards
●Detailed chunking strategy specification with semantic boundaries, metadata preservation, and overlap handling for different document types
●Hybrid retrieval pipeline design combining dense vector search, sparse keyword matching, and metadata filtering with relevance scoring

Not designed for ↓

×General chatbot development without retrieval components
×Building traditional search engines or keyword-only systems
×Customer service process design or agent training programs
×Generic LLM fine-tuning without retrieval augmentation

Expects

Detailed requirements about existing knowledge sources, support ticket taxonomy, query volume, and current resolution workflows that need RAG enhancement.

Returns

Complete RAG system architecture with ingestion pipelines, chunking strategies, retrieval components, answer generation flows, and quality monitoring frameworks.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

ragretrieval-augmented-generationcustomer-supportknowledge-basevector-searchhybrid-searchchunkingsupport-automationticket-resolutionknowledge-managementllm-applicationssemantic-search

Research Foundation: 8 sources (3 official docs, 2 academic, 2 industry frameworks, 1 web)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Works well with

Embedding Space ArchitectPlatinum Hybrid Search ArchitectGold Knowledge Graph ArchitectGold LLM Evaluation Framework DesignerGold Vector Database OptimizationGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

RAG Architecture DesignerGold RAG Chunking StrategistGold Search Relevance EngineerPlatinum

Common Workflows

Support System RAG Implementation

Complete RAG system design, vector database optimization, and continuous evaluation framework for production support operations

rag-support-ops-specialist→Vector Database Optimization→LLM Evaluation Framework Designer

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