Prompt Engineering Roadmap

A structured path from understanding how LLMs work to designing production-grade prompt systems, evaluation pipelines, and AI-powered applications.

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1. Stage 1: LLM Foundations

How Large Language Models Work
Understanding token prediction grounds every prompting decision you make.
videoIntro to Large Language Models – Andrej Karpathy docOpenAI – How ChatGPT Works
Tokens, Context Windows & Temperature
These parameters directly control output quality and cost.
docOpenAI Tokenizer Tool docOpenAI API – Parameters Reference
The Prompt as an Interface
Framing prompts as UI/API contracts clarifies engineering goals.
tutorialLearn Prompting – Basics

Zero-Shot & Few-Shot Prompting
Few-shot examples dramatically improve model accuracy on new tasks.
tutorialLearn Prompting – Few-Shot Prompting docPrompt Engineering Guide – Few-Shot
Chain-of-Thought (CoT) Prompting
CoT unlocks multi-step reasoning in complex tasks.
docPrompt Engineering Guide – Chain-of-Thought tutorialLearn Prompting – Chain of Thought
Role & System Prompt Design
System prompts set consistent model behavior across all interactions.
Instruction Formatting & Delimiters
Structured delimiters reduce ambiguity and hallucination rates.
tutorialPrompt Engineering Guide – Prompt Structure

Tree of Thoughts & Self-Consistency
These techniques push model accuracy on hard reasoning benchmarks.
docPrompt Engineering Guide – Tree of Thoughts docPrompt Engineering Guide – Self-Consistency
ReAct: Reasoning + Acting
ReAct is the backbone pattern for tool-using AI agents.
docPrompt Engineering Guide – ReAct
Retrieval-Augmented Generation (RAG) Prompting
RAG grounds LLM answers in real data, reducing hallucinations.
docPrompt Engineering Guide – RAG tutorialLangChain RAG Tutorial
Prompt Chaining & Pipelines
Chaining decomposes complex tasks into reliable, testable steps.
tutorialLangChain – Chains Conceptual Guide

OpenAI Chat Completions API
Hands-on API use turns theory into deployable prompt systems.
docOpenAI – Chat Completions Guide
Function Calling & Structured Outputs
Structured outputs make LLMs reliable components in real systems.
docOpenAI – Structured Outputs Guide docOpenAI – Function Calling Guide
LangChain & LlamaIndex Prompt Templates
Frameworks abstract prompt management for production-scale apps.
docLangChain – Prompt Templates Docs docLlamaIndex – Prompts Guide
Working with Anthropic Claude API
Multi-provider fluency is expected in professional prompt engineering roles.
docAnthropic – Prompt Engineering Overview

Prompt Evaluation & Benchmarking
Systematic evals replace guesswork with measurable prompt quality.
tutorialPrompt Engineering Guide – Evaluating Prompts
Hallucination Detection & Mitigation
Hallucination control is a core production reliability requirement.
Adversarial Prompting & Jailbreaks
Understanding attacks is required to build safe, robust systems.
docPrompt Engineering Guide – Adversarial Prompting
Guardrails & Content Moderation
Production AI must enforce safe output boundaries programmatically.
docOpenAI Moderation API docGuardrails AI Docs

Building LLM Agents with Tool Use
Agents are the dominant architecture for autonomous AI applications.
docOpenAI – Agents SDK Overview tutorialLangChain – Agents Conceptual Guide
Model Context Protocol (MCP)
MCP is the emerging standard for connecting LLMs to external tools.
docAnthropic – MCP Introduction
Prompt Versioning & Management
Version control for prompts enables safe iteration in production.
docLangSmith – Prompt Hub
Cost Optimization & Latency Tuning
Production engineers balance capability, speed, and API spend.
docOpenAI – Model Selection & Cost Guide

Building a Prompt Engineering Portfolio
Demonstrated projects signal employer-ready practical skill.
Contributing to Open-Source LLM Projects
Open-source contributions provide credibility and real collaboration experience.
docHugging Face – Open Source Community
Prompt Engineering Interview Prep
Targeted prep on common interview patterns accelerates job placement.
tutorialPrompt Engineering Guide – Full Reference
Staying Current with LLM Research
The field evolves weekly; continuous learning is non-negotiable.
docHugging Face Papers tutorialAndrej Karpathy – Neural Networks Zero to Hero

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