• Understand the fundamentals of prompt operations and their necessity

• Set up PromptOps in a production-ready environment

• Manage prompt versioning, testing, and approvals

• Integrate CI/CD pipelines for LLM prompts

• Perform A/B and multivariate testing on prompt performance

• Track user feedback and integrate prompt updates accordingly

• Implement role-based access and governance

• Connect PromptOps with LLM APIs (OpenAI, Anthropic, etc.)

• Deploy prompts in scalable AI applications

• Monitor and improve prompt performance using observability tools

Course Syllabus

1. Introduction to PromptOps & the PromptOps Mindset

2. Prompt Engineering: From Experimentation to Operations

3. Installing and Configuring PromptOps in Your Stack

4. Version Control for Prompts: Strategies and Tools

5. CI/CD for Prompts: Automating Deployment and Rollbacks

6. Integrating PromptOps with OpenAI, Anthropic, and LangChain

7. Testing Prompts: A/B Testing, Regression Testing, and Feedback Loops

8. Monitoring Prompt Quality and Response Metrics

9. Prompt Governance and Role-based Access Control

10. Case Study: Using PromptOps in a RAG-enabled System

11. Connecting PromptOps with PromptLayer, Ragas, and Helicone

12. Troubleshooting, Debugging, and Best Practices

Upon successful completion of the PromptOps course, you will receive a Uplatz Certificate of Completion that verifies your skills in AI prompt operations and DevOps-style lifecycle management for LLM prompts. This certification demonstrates your ability to implement enterprise-grade workflows using PromptOps, integrate it with the modern LLM stack, and maintain high levels of observability, compliance, and performance. This certification is a valuable asset for AI professionals, engineers, and consultants involved in productionizing AI models.

Mastering PromptOps opens the door to various roles at the intersection of machine learning, DevOps, and AI infrastructure. As organizations continue scaling their LLM applications, the demand for professionals who can build and manage reliable prompt systems is surging.

You can pursue roles such as:

• Prompt Engineer

• AI Operations Engineer

• LLMOps Specialist

• ML Infrastructure Engineer

• AI Product Manager

• Prompt Lifecycle Consultant

• DevOps for AI Specialist

Industries like e-commerce, finance, healthcare, and SaaS are now deploying LLMs for automation, summarization, chatbots, and search enhancement—making prompt reliability mission-critical. By learning PromptOps, you're equipping yourself with future-proof skills that bridge AI experimentation and real-world deployment.

1. What is PromptOps used for?
   PromptOps is used to manage, version, deploy, and monitor prompts in large language model workflows.

2. How does PromptOps differ from PromptLayer?
   PromptOps focuses on automation, governance, and CI/CD, while PromptLayer is primarily used for observability and tracking.

3. What role does version control play in PromptOps?
   Version control ensures reproducibility, audit trails, and rollback capabilities for prompt updates.

4. Can you integrate PromptOps with LangChain?
   Yes, PromptOps integrates with LangChain to manage prompts as part of LLM-based pipelines.

5. What is A/B testing in prompt workflows?
   It refers to evaluating two or more prompts to determine which performs better based on user feedback or metrics.

6. What kind of prompts does PromptOps manage?
   It manages system prompts, user prompts, instruction-based prompts, and more used in LLM interactions.

7. What is CI/CD in the context of prompts?
   CI/CD refers to automating testing and deployment of prompts to ensure frequent and reliable updates.

8. Is PromptOps only for OpenAI models?
   No, it supports integration with various LLM providers including OpenAI, Anthropic, Cohere, etc.

9. What observability tools complement PromptOps?
   PromptLayer, TruLens, Ragas, and Helicone are commonly used for monitoring LLM behavior.

10. Why is PromptOps important in production environments?
    It ensures that prompt changes are controlled, tested, and traceable—reducing risk and improving model reliability.