A fraud team may use a large language model to summarize flagged cases for investigators. LLMOps is the ongoing work of keeping that system’s data, retrieval, and model components useful and controlled—not just putting a model behind an interface. In this workflow, analysts validate summaries rather than treating them as automated fraud decisions. section 7.4.3
For a fraud-detection system using retrieval-augmented generation (RAG), the operational work includes:
- Curate and update fraud policies, case notes, investigation manuals, Suspicious Activity Report templates, regulatory updates, and relevant threat intelligence; scrub or anonymize personally identifiable information before adding documents to the retrieval corpus. section 7.4.3
- Split documents into meaningful chunks, preserving useful boundaries such as legal sections or policy clauses. section 7.4.3
- Select, deploy, and version the embedding model; keep the models used for indexing and querying consistent. section 7.4.3
- Maintain the vector database as documents change. Apply retrieval filters such as date, source, and access permissions, while accounting for security, latency, scale, data residency, and compliance. section 7.4.3
- Consider hybrid search or re-ranking when simple similarity search does not adequately prioritize relevant, authoritative, recent material. section 7.4.3
Keep the person responsible for the decision
In a financial-post moderation workflow, AI flags potentially problematic posts and supplies a reasoning summary, but a moderator makes the final decision. Clear guidelines protect moderators from being penalized for the AI’s initial misclassifications. Cross-training existing data engineers in practical LLMOps addresses the need for staff who can work with complex LLM pipelines. section 2.5.3
Operational success depends on more than model output. Fraud summaries must support review, while moderation workflows need human authority and organizational trust. These examples outline responsibilities and controls, not a production-ready implementation. section 2.5.3 section 7.4.3
