GenAI Implementation Lifecycle & Best Practices
A focused, actionable guide to the end-to-end lifecycle and best practices for implementing Generative AI in ShieldCraft AI, tailored for high-assurance, enterprise-grade security applications.
Purpose
This guide details the end-to-end lifecycle and best practices for implementing Generative AI in ShieldCraft AI, from use case discovery to production MLOps. It is focused, actionable, and tailored for high-assurance, enterprise-grade security applications.
Lifecycle Stages
- Use Case Discovery & Success Criteria
- Identify where GenAI delivers the most value for ShieldCraft AI.
- Define clear, measurable outcomes and ensure all use cases are security-relevant and high-impact.
- Data Preparation & Retrieval
- Ground GenAI in high-quality, relevant security data.
- Build robust pipelines for ingest, clean, and structure data for RAG and LLMs.
- Model Selection & Prototyping
- Select, integrate, and rapidly prototype with LLMs and RAG pipelines.
- Focus on measurable, iterative improvement.
- Application Integration & Orchestration
- Build robust, production-ready application logic that leverages GenAI.
- Integrate with APIs, dashboards, and ensure reliability.
- Evaluation, Testing & Continuous Improvement
- Continuously evaluate and refine GenAI performance using both automated and human-in-the-loop feedback.
- Deployment, MLOps & Monitoring
- Operationalize GenAI for reliability, scalability, and security in production.