Ad Astra Software | AI-led software, services, consulting, and managed platforms

Publication date: December 12, 2025 · 6 min reading

İsmail Yurtsever

Ad Astra

Prompt Engineering LLM Security

Methods we use in prompt engineering workshops; While quickly validating customer scenarios, it does not ignore security and compliance requirements.

5 steps

Scenario framework

Discovery and verification steps we use in workshops.

3 levels

Control layer

Structure consisting of functional, security and accessibility tests.

Multi-provider

LLM integrations

Modular architecture that works with Azure OpenAI, Vertex AI and Claude.

Match the scenario with risk matrices

The first step of the workshops is to extract critical touchpoints in the customer journey and prepare risk/impact matrices for each.

This work allows us to define quality criteria and feedback channels for each scenario that will enter the prompt library.

Prompts are stored in Git-based repositories and tracked with release notes and automated tests.

Guardrail tests measure risks such as toxicity and PII leakage; Failed prompts are rearranged according to runbooks.

We regularly measure performance using Azure OpenAI Evaluation, Anthropic Constitutional AI, or custom metrics.

Human monitoring and automated reporting combine to enable teams to iterate quickly.

Prompt engineering outputs should be evaluated together with the user scenario and risk analysis.
Without versioning and guardrail testing, the prompt library cannot scale.
Continuous improvement is possible when automatic evaluation is operated in conjunction with human supervision.

Our service package focused on prompt engineering and rapid prototyping.

Platform that supports AI scenario orchestration for multilingual experiences.

Accelerate your LLM investments with our workshops covering scenario discovery, guardrail design and evaluation automation.