Advanced Prompt Engineering for Developers

Build Production-Grade LLM Systems

Advanced Prompt Engineering for Developers is designed for engineers who want to move beyond basic prompts and build reliable, scalable, and cost-efficient AI systems. This course focuses on the engineering discipline behind prompt design—treating prompts as structured, testable, and versioned components of modern software architecture.

You will learn how to design prompt hierarchies, control model behavior with precision, and integrate large language models into real-world applications using APIs, structured outputs, and tool-calling workflows.

From Simple Prompts to Robust Architectures

Most developers start with trial-and-error prompting. This course replaces guesswork with systematic design patterns. You will master:

• Instruction layering and system role control
• Chain-of-thought and reasoning strategies
• Dynamic context injection and retrieval-augmented generation
• Schema-constrained outputs using JSON validation
• Reusable, modular prompt libraries

By the end, you will think of prompts as composable building blocks within larger AI systems.

Reliability, Evaluation, and Optimization

Advanced AI applications require consistency and measurable quality. You will implement evaluation pipelines, automated prompt testing, and A/B experimentation frameworks. The course also covers:

• Hallucination detection and mitigation techniques
• Grounding responses with external data sources
• Latency and token cost optimization
• Security and prompt injection defense

These skills are essential for deploying LLM-powered features in production environments.

Tool Use, Agents, and Multi-Step Workflows

Modern LLM applications go beyond text generation. You will build tool-aware systems using function calling and multi-step agent pipelines. Learn how to orchestrate reasoning steps, integrate APIs, and design robust fallback logic for real-world reliability.

The capstone project guides you through building a production-ready LLM microservice with monitoring, logging, and versioned prompts.

Who This Course Is For

This course is ideal for software developers, backend engineers, AI engineers, and technical founders who want to integrate LLMs into products. If you are already familiar with APIs and programming, this course will elevate your prompt engineering to an advanced, system-level discipline.

Ready to engineer intelligent systems with confidence? Register free or browse all courses to continue advancing your AI expertise with Edu AI.

What You Will Learn

• Design advanced prompt architectures for complex LLM workflows
• Apply system, role, and context engineering techniques
• Implement chain-of-thought and structured reasoning patterns
• Build reusable prompt templates and modular prompt libraries
• Control hallucinations and improve factual reliability
• Use function calling and tool integration effectively
• Evaluate and benchmark prompt performance quantitatively
• Optimize prompts for latency, cost, and token efficiency
• Design multi-agent and multi-step LLM pipelines
• Deploy production-ready prompt systems with monitoring

Requirements

• Strong Python or JavaScript programming experience
• Basic understanding of APIs and RESTful services
• Familiarity with large language models like GPT
• Comfort with JSON and structured data formats