Prompt Ops:

The Prompt Engineering Lifecycle

"Prompt Ops: The Prompt Engineering Lifecycle" is the definitive, first-of-its-kind textbook that systematically addresses the critical need for operationalizing prompt engineering. As generative AI transitions from research labs to mainstream enterprise applications, the ad-hoc methods of managing prompts are no longer sufficient. This book introduces "Prompt Ops," a comprehensive framework that applies the robust, battle-tested principles of DevOps and MLOps to the complete lifecycle of prompts—from design and development to testing, deployment, and ongoing monitoring.

This book serves as a comprehensive guide for students and professionals, bridging the gap between the creative art of prompt design and the engineering discipline required to manage them at scale. It provides a structured, step-by-step methodology for building reliable, efficient, and maintainable AI systems powered by Large Language Models (LLMs).


Key Features of this Book

1. Hands-On Practicals: Every chapter includes practical, code-based examples and labs using industry-standard tools like Python, Git, Docker, and popular LLM APIs, allowing readers to apply concepts immediately.
2. End-to-End Lifecycle Coverage: This is the only book that covers the entire Prompt Ops lifecycle: from prompt design and versioning, through automated testing and evaluation, to CI/CD, production monitoring, and governance.
3. Real-World Case Studies: Features insightful case studies from various domains (e.g., customer support, content creation, code generation) to illustrate how Prompt Ops principles are applied in practice.
4. Industry-Standard Tooling: Focuses on practical implementation using tools that professionals use every day, including Git for version control, Pytest for testing, GitHub Actions for CI/CD, and frameworks like LangChain, alongside prompt management platforms.



To Whom is this Book For?

1. B.Tech and M.Tech Computer Science Students: Serves as a core textbook for courses on AI, Machine Learning, and Software Engineering, perfectly aligned with NEP 2020 and AICTE syllabi by focusing on practical skills, project-based learning, and industry relevance.
2. AI/ML Engineers: Provides the essential toolkit for moving beyond model training and into the operational realities of deploying and managing prompt-driven applications.
3. DevOps and MLOps Engineers: Offers a clear pathway to extend their expertise in automation, CI/CD, and monitoring to the unique challenges of the generative AI stack.
4. Data Scientists and NLP Practitioners: Helps them understand how their carefully crafted prompts can be deployed and maintained in production environments with rigour and reliability.
5. Software Developers and Architects: Equips them with the knowledge to integrate LLMs into their applications in a scalable and systematic manner.
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