Prompt Engineering & AI Agents for Developers
Course Overview
Prompt Engineering & AI Agents for Developers is a hands-on course for developers who want to move beyond casual chatbot use and start building real products on top of large language models (LLMs). You'll learn how prompting actually works under the hood, how to design prompts that produce reliable, structured output, and how to wire those prompts into autonomous AI agents that can call tools, remember context, and complete multi-step tasks.
What You'll Learn
- How LLMs process prompts and why that shapes every technique in this course
- Core prompting patterns: zero-shot, few-shot, and chain-of-thought reasoning
- How to get consistent, structured (JSON) output your code can actually parse
- Retrieval-Augmented Generation (RAG) for grounding answers in your own data
- How to design, test, and evaluate prompts like you would test code
- What separates an "agent" from a chatbot, and how tool-calling works
- Agent memory, state, and multi-agent orchestration patterns
- Guardrails, safety, cost, and latency tradeoffs for production systems
- How to deploy and monitor an AI agent in a real application
Who This Course Is For
This course is built for developers, not prompt hobbyists. If you're comfortable writing code and want a structured, practical path into building LLM-powered features and autonomous agents, this course is for you.
Prerequisites
Basic programming experience (any language) and familiarity with using an API. No prior AI/ML background required.
कोर्स करिकुलम
4 सेक्शन्स · 16 क्यूरेटेड पाठFoundations of Prompt Engineering
Build an accurate mental model of how LLMs work and master the core prompting techniques every other lesson builds on.
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Foundations of Prompt Engineering
Build an accurate mental model of how LLMs work and master the core prompting techniques every other lesson builds on.
- What Is Prompt Engineering and Why It Matters for Developers
- How Large Language Models Actually Work: A Developer's Mental Model
- Core Prompting Techniques: Zero-Shot, Few-Shot, and Chain-of-Thought
- Structuring Prompts with System, User, and Assistant Roles
Advanced Prompting Patterns
Move from one-off prompts to production-grade patterns: templates, structured outputs, RAG, and systematic evaluation.
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Advanced Prompting Patterns
Move from one-off prompts to production-grade patterns: templates, structured outputs, RAG, and systematic evaluation.
- Prompt Templates and Reusable Patterns for Production Apps
- Structured Outputs: Getting Reliable JSON and Function Calls from LLMs
- Retrieval-Augmented Generation (RAG): Grounding Prompts in Real Data
- Evaluating and Testing Prompts Like Code
Building AI Agents
Go from single-shot prompting to autonomous agents: tool use, memory, state, and multi-agent orchestration.
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Building AI Agents
Go from single-shot prompting to autonomous agents: tool use, memory, state, and multi-agent orchestration.
- What Makes an "Agent" Different From a Chatbot
- Tool Use and Function Calling: Giving Agents Real-World Actions
- Agent Loops, Memory, and State Management
- Multi-Agent Systems and Orchestration Patterns
Shipping AI Agents to Production
Make agents production-ready: safety guardrails, cost and latency tradeoffs, monitoring, and a full end-to-end case study.
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Shipping AI Agents to Production
Make agents production-ready: safety guardrails, cost and latency tradeoffs, monitoring, and a full end-to-end case study.
- Guardrails, Safety, and Handling Hallucinations
- Cost, Latency, and Model Selection Tradeoffs
- Deploying and Monitoring AI Agents in Real Applications
- Case Study: Building a Simple Coding Assistant Agent End-to-End