Prompt Engineering for Beginners: Learn by Coding 9 Prompting Techniques :: Ep-03

A complete series teaching you how to move from demo AI agents to real production-ready AI agents. In the first series, we learned the foundation of AI agents — OpenAI API calls, function calling, structured outputs, agent loops, MCP, A2A, local LLMs, and multi-protocol agents. Now in this new series, we go deeper into Production Agent Engineering. We will learn how to build AI agents that are reliable, debuggable, testable, safer, cheaper, and closer to real-world production systems. This series covers production logging, prompt engineering, ReAct prompting, planning, reflection, LangGraph workflows, human-in-the-loop approval, OpenAI Agents SDK, agent harnesses, evaluation, LLM-as-a-Judge, guardrails, and real project builds. 🔗 All code is open source: [GitHub link — add yours] ☕ Support the series: [optional — add later] 📚 What you'll learn in this series ✓ Why demo agents are not production agents ✓ What to log in every AI agent call ✓ Track request IDs, session IDs, tokens, latency, errors, tool calls, and cost ✓ Build production logging in Python using JSONL and SQLite ✓ Learn prompt engineering by coding and testing prompts yourself ✓ Understand zero-shot, few-shot, Chain of Thought, Tree of Thought, self-consistency, role prompting, structured output, and prompt chaining ✓ Write better prompts for AI agents using ReAct, planning, reflection, and guardrails ✓ Improve weak agents using structured prompts ✓ Understand LangGraph for stateful agent workflows ✓ Build LangGraph agents with nodes, edges, state, and conditional routing ✓ Add human approval before risky actions ✓ Build multi-agent coding workflows ✓ Understand OpenAI Agents SDK ✓ Use agents, tools, handoffs, guardrails, sessions, and tracing ✓ Understand what an agent harness is and why production agents need one ✓ Build an agent harness to run repeatable test cases ✓ Capture agent inputs, outputs, tool calls, logs, traces, and metrics ✓ Evaluate AI agents using golden datasets ✓ Measure accuracy, tool correctness, latency, and cost ✓ Use LLM-as-a-Judge for open-ended evaluation ✓ Add input, output, and tool guardrails ✓ Build safer AI agents with permission checks and fallbacks ✓ Build real-world projects like coding agents and support agents 🎯 Who this is for Developers who already know basic LLM API calls Engineers who want to move from toy agents to production agents AI engineers learning prompt engineering, LangGraph, guardrails, harnesses, and evaluation Developers building real AI agent applications Anyone who wants practical Python code instead of only theory 📺 Series episodes 00:00 Intro [Update timestamps as you publish:] Ep 1 — Production Logging for AI Agents: What You Must Track    • Production Logging for AI Agents: What You...   Ep 2 — Build Production Logging for AI Agents in Python    • Build Production Logging for AI Agents in ...   Ep 3 — Prompt Engineering for Beginners: Learn by Coding 9 Prompting Techniques Ep 4 — Prompt Engineering for AI Agents: ReAct, Planning, Reflection, and Guardrails Ep 5 — Improve an AI Agent with Better Prompts Ep 6 — LangGraph Explained: Why Agent Workflows Need Graphs Ep 7 — Build a Stateful AI Agent with LangGraph Ep 8 — Human-in-the-Loop AI Agents: Approval Before Dangerous Actions Ep 9 — Add Human Approval to an AI Agent in Python Ep 10 — I Built a Multi-Agent Coding Team in Python Ep 11 — OpenAI Agents SDK: Agents, Tools, Handoffs, and Tracing Ep 12 — Build an Agent with OpenAI Agents SDK in Python Ep 13 — Agent Harness Explained: Test Your Agent Like a System Ep 14 — Build an Agent Harness in Python Ep 15 — How to Evaluate AI Agents: Accuracy, Tools, Cost, Latency Ep 16 — Build an AI Agent Evaluation Framework in Python Ep 17 — LLM-as-a-Judge: Evaluate Agent Answers Automatically Ep 18 — Guardrails for AI Agents: Stop Bad Inputs, Outputs, and Tool Calls Ep 19 — Add Guardrails to an AI Agent in Python Ep 20 — Build a Customer Support AI Agent with RAG and Tools 🛠️ Tools used in the series Python 3.11+ OpenAI SDK OpenAI Responses API Function calling Structured outputs Prompt engineering ReAct prompting Chain of Thought Tree of Thought Self-consistency JSONL logging SQLite LangGraph OpenAI Agents SDK Agent harness Golden datasets Agent evaluation LLM-as-a-Judge Guardrails RAG FastAPI / Streamlit in later projects Agent Loop → Logging → Prompt Engineering → ReAct → LangGraph → Human Approval → Agents SDK → Agent Harness → Evaluation → Guardrails → Real Projects The goal is simple: By the end of this series, you should be able to say: “I can build, debug, test, evaluate, and improve real AI agents.” 💬 Got questions? Drop them in the comments — I read every one. 🔔 Subscribe so you don't miss the next episodes in the Production AI Agent Engineering Series. #AIagents #AgenticAI #PromptEngineering #OpenAI #LangGraph #Python #LLM #AgentsSDK #RAG #Guardrails #AgentEvaluation #AIAgentsTutorial #LearnToCode