Cover of Prompt Engineering for Production Systems by Vishal Sharma

E-book · First edition, August 2026

Prompt Engineering for Production Systems

Building reliable, testable and maintainable LLM applications

by Vishal Sharma

Shipping a working prototype on a frontier model takes an afternoon. Shipping it as a reliable, observable, audited production system takes a quarter. This book closes that gap by treating prompts the way we already treat code: versioned, tested, monitored, secured and governed.

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Who it is for

Engineers, data scientists, ML practitioners and technical leads who have moved past the demo stage. You can already get an LLM to do something useful; what you need now is the discipline and tooling to make it dependable.

You do not need a research background, but you should be comfortable reading code and thinking about systems. Managers and product leaders get a non-technical section at the end of every chapter.

How each chapter works

  • A real production failure
  • The concept that prevents it
  • A technical implementation you can adapt
  • A short section framed for your team or manager
  • A checklist or template to take away

Code is model-agnostic wherever possible, so the practices outlast any single provider or model version.

Contents

  1. –PrefaceFree
  2. 1Why Prompts Fail in ProductionFree
  3. 2The Production MindsetFree
  4. 3Prompt Architecture and Patterns
  5. 4Versioning and Change Management
  6. 5Evaluation Frameworks
  7. 6Regression Testing
  8. 7Cost, Latency and Token Economics
  9. 8Reliability Engineering
  10. 9Observability for LLM Systems
  11. 10Chains, Agents and Multi-step Prompting
  12. 11Security and Prompt Injection
  13. 12Governance, Compliance and Team Workflows
  14. 13Prompt Pattern Library
  15. 14Evaluation Checklist and Templates
  16. 15Production Readiness Checklist
  17. 16Glossary

Start reading chapter 1

About the author

Vishal Sharma is a data scientist with around a decade of experience in analytics, machine learning and application development in financial services and insurance, and a PhD researcher in computer science working on multimodal AI. He has spent the last several years building and maintaining LLM features under real traffic, real cost ceilings and real audit requirements. This book grew out of the checklists and post-mortems that accumulated along the way. More about Truth with Data.

Questions

How do I get the book after paying?

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Can I read online on more than one device?

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Which currency will I pay in?

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What if it is not for me?

Read the preface and the first two chapters free before you buy. If something goes wrong with your purchase, email truthwithdata@gmail.com and we will sort it out.

Can I buy it on Amazon instead?

Yes, the Kindle edition is on Amazon and reads in the Kindle app and on Kindle devices. Buying here gets you the online edition, PDF and EPUB together; the Amazon purchase covers the Kindle edition only.

Found an error?

Corrections are welcome at truthwithdata@gmail.com and are credited in later editions.

Make your prompts production-ready

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