SA

Feature · Prompt registry

Bring prompts into the governed operating model, not just application code

SentinelAI gives teams a prompt registry for versioned prompt assets with lifecycle state, retrieval configuration, target-model context, linked AI systems, and evaluation-aware review signals.

Book a demoRead docsStart trial

What this area covers

Prompt registry workflows help teams treat prompts as governed assets instead of ad hoc strings buried in repos and tickets. The registry keeps prompt versions, templates, system prompts, variables, test cases, and linked runtime dependencies visible to governance stakeholders.

Related product areas

  • AI systems

    Track governed runtime systems that combine models, approved use cases, datasets, release state, and readiness into one operational record.

  • RAG sources

    Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.

  • Evaluation suites

    Define governed prompt evaluation suites with baselines, regression thresholds, run evidence, and release-blocking posture.

  • Release governance

    Manage AI-system release records with approval state, rollback references, dependency snapshots, and invalidation handling.

  • Semantic governance

    Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.

  • LLM telemetry and monitoring

    Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.

Core capabilities

Built to support production governance work

Versioned prompt records

Track prompt name, version, kind, owner, lifecycle status, and target-model context so teams can review prompt changes with a durable record.

Template and system-prompt visibility

Keep the prompt template, system prompt, variables schema, metadata, and test cases together instead of scattering them across separate tools.

Linked AI systems and RAG sources

Associate prompts with the AI systems and governed retrieval sources they influence so reviewers can understand operational dependencies quickly.

Retrieval configuration context

Preserve retrieval settings and supporting metadata for prompts that depend on grounded responses or governed source collections.

Evaluation-aware posture

Surface the latest evaluation score and keep prompt-level test cases ready for evaluation suites and release decisions.

Target users

  • Prompt and application owners managing versioned instructions across production AI systems
  • AI governance teams that need prompt changes captured in a structured reviewable record
  • ML and platform teams coordinating prompts with evaluation suites, releases, and retrieval dependencies
  • Risk and assurance stakeholders reviewing how prompts influence deployed system behavior

Governance value

  • Reduces prompt sprawl by creating a governed inventory of reusable prompt assets
  • Improves traceability between prompt changes, linked systems, and retrieval dependencies
  • Supports evaluation and release workflows with durable prompt metadata instead of ad hoc context gathering
  • Makes prompt review more explainable for governance, legal, and assurance stakeholders
  • Keeps prompt operations aligned to the same operating model as models, datasets, and releases

How teams use it

A practical operating flow for this feature family

Step 1

Register prompt assets

Capture the prompt version, owner, kind, template, variables, and target-model context as soon as the asset enters review.

Step 2

Link runtime and retrieval dependencies

Associate each prompt with the AI systems and governed RAG sources that rely on it so governance context stays connected.

Step 3

Carry prompts into evaluation and release

Use the prompt record as the source of truth for test cases, evaluation posture, and downstream release-governance decisions.

Continue exploring

Explore how SentinelAI connects adjacent governance workflows

AI systems

Track governed runtime systems that combine models, approved use cases, datasets, release state, and readiness into one operational record.

RAG sources

Register governed retrieval sources with ingestion status, version history, citation context, and AI-system linkage.

Evaluation suites

Define governed prompt evaluation suites with baselines, regression thresholds, run evidence, and release-blocking posture.

Release governance

Manage AI-system release records with approval state, rollback references, dependency snapshots, and invalidation handling.

Semantic governance

Operate taxonomy, ontology, relationship, and graph-backed governance workflows across models, use cases, datasets, controls, and evidence.

LLM telemetry and monitoring

Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.