Generative AI & LLM Operations
An executive and technical brief on production LLM architecture, retrieval, evaluation, security, observability, ownership, delivery risk and operating stages.
An executive and technical brief on production LLM architecture, retrieval, evaluation, security, observability, ownership, delivery risk and operating stages.
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Overview
On this page
Production generative AI is an application and operating-system decision—not a model-selection exercise. Leaders need an architecture that keeps data, evaluation, security, reliability and economics under accountable control.
The central decision is where generative AI belongs in a business workflow and what control boundary surrounds it. Model capability changes rapidly; enterprise advantage sits in governed information, workflow integration, evaluation assets, user adoption and the ability to operate change safely.
Choose the workflow, consequence level and ownership before choosing a model. This establishes whether the service drafts, recommends, decides or acts—and therefore which data, human review, security and service controls are required.
If the organization cannot name the workflow owner, acceptance evidence, prohibited behaviour and operational owner, the initiative is not ready for production architecture.
Common enterprise patterns include knowledge assistance, document handling, service-agent support, software-development assistance, search, summarization and controlled tool execution. Each pattern has different evidence, latency, privacy and human-control requirements.
A retrieval assistant depends on source ownership and permission-aware search. A drafting assistant depends on review and provenance. A tool-using agent requires strict authorization, bounded actions and transaction controls. One generic platform configuration does not satisfy every pattern.
Keep deterministic business rules outside the model where feasible. Use structured outputs and validation at system boundaries. Treat retrieved documents, user prompts and tool responses as untrusted input. Separate conversation memory from authoritative business records.
- Golden datasets
- Task-specific scoring
- Failure categories
- Regression comparison
- Domain review
- Policy-sensitive judgement
- User usefulness
- Residual-risk acceptance
- Task completion
- Latency and reliability
- Grounding and fallback
- Human override and rejection
- Prompt and model version
- Retrieval configuration
- Evaluation result
- Release and rollback owner
- Classify prompts, retrieved data, outputs, memory and logs.
- Enforce source-system permissions during retrieval.
- Protect system instructions, secrets and tool credentials.
- Test prompt injection, data disclosure, unsafe output and tool misuse.
The executive decision
The central decision is where generative AI belongs in a business workflow and what control boundary surrounds it. Model capability changes rapidly; enterprise advantage sits in governed information, workflow integration, evaluation assets, user adoption and the ability to operate change safely.
Choose the workflow, consequence level and ownership before choosing a model. This establishes whether the service drafts, recommends, decides or acts—and therefore which data, human review, security and service controls are required.
If the organization cannot name the workflow owner, acceptance evidence, prohibited behaviour and operational owner, the initiative is not ready for production architecture.
Business problem and operating context
Common enterprise patterns include knowledge assistance, document handling, service-agent support, software-development assistance, search, summarization and controlled tool execution. Each pattern has different evidence, latency, privacy and human-control requirements.
A retrieval assistant depends on source ownership and permission-aware search. A drafting assistant depends on review and provenance. A tool-using agent requires strict authorization, bounded actions and transaction controls. One generic platform configuration does not satisfy every pattern.
Reference architecture and technical considerations
Keep deterministic business rules outside the model where feasible. Use structured outputs and validation at system boundaries. Treat retrieved documents, user prompts and tool responses as untrusted input. Separate conversation memory from authoritative business records.
Evaluation, observability and acceptance
Model metrics alone do not represent service quality. Monitor the end-to-end workflow, including retrieval, tool calls, validation, human handling and downstream system outcomes.
MENTARA uses OpenTelemetry as a vendor-neutral instrumentation reference for end-to-end traces, metrics and logs.
- Golden datasets
- Task-specific scoring
- Failure categories
- Regression comparison
- Domain review
- Policy-sensitive judgement
- User usefulness
- Residual-risk acceptance
- Task completion
- Latency and reliability
- Grounding and fallback
- Human override and rejection
- Prompt and model version
- Retrieval configuration
- Evaluation result
- Release and rollback owner
Security, privacy and governance
MENTARA uses the NIST AI Risk Management Framework: Generative AI Profile to structure governance and the OWASP GenAI Security Project to inform security testing across the generative-AI lifecycle.
- Classify prompts, retrieved data, outputs, memory and logs.
- Enforce source-system permissions during retrieval.
- Protect system instructions, secrets and tool credentials.
- Test prompt injection, data disclosure, unsafe output and tool misuse.
- Apply least privilege and transaction validation to every tool action.
- Define retention, deletion, geographic and model-training restrictions.
- Record system, model, data, risk and business owners.
- Maintain an incident route that covers security, privacy, quality and provider failure.
Delivery stages
Bound the workflow, users, consequence, prohibited behaviour, owner and value evidence.
Define data flow, model strategy, retrieval, tools, human controls, security and service budgets.
Build golden datasets, automated checks, domain review and explicit acceptance evidence.
Complete observability, runbooks, incident paths, rollback and controlled user exposure.
Review value, quality, risk, service and economics together; re-evaluate material change.
Decision checklist
- The business workflow and product owner are named.
- The role of the model is separated from deterministic rules and controls.
- Data purpose, access, retention and provenance are governed.
- Evaluation reflects task consequence and real failure modes.
- Human review and tool authorization are explicit.
- Latency, reliability, recovery and cost budgets are agreed.
- Production monitoring connects model behaviour to workflow outcomes.
- Model, application, data, security and operations ownership is funded.
Design LLM operations around a bounded workflow.
A named MENTARA lead can help translate a use case into architecture, control, evaluation and operating-model decisions.
Continue with the decision in front of you.
Share the business context, constraints and expected outcome. MENTARA will identify the relevant accountable route.
Submit your requirement