Prompt Engineering

Prompt Engineering Roadmap

Step by step guide to learning Prompt Engineering in @currentYear@

This page is a visitor-friendly snapshot of how William currently sees this area: what feels strong, what is still being refined, and where the evidence is still partial.

0

Done

0

Learning

0

Target

0

Skipped

47

Tracked

Status meanings

Done: enough evidence to present this area as a solid strength.

Learning: an area of active development with growing confidence.

Target: relevant and intentional, but still earlier in the journey.

Skipped: not a current priority relative to other areas.

How the estimates are formed

Profile and manual review: 0

Evidence from project experience: 0

Competency mapping alignment: 0

Quiz-backed updates: 0

These signals help estimate progress, but they are still best treated as directional rather than perfectly complete.

Areas currently being strengthened

No standout growth areas are being highlighted here yet.

Introduction

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LLMs and how they work?

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What is a Prompt?

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What is Prompt Engineering?

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OpenAI

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Google

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Anthropic

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Meta

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xAI

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LLM

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Tokens

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Context Window

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Hallucination

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Model Weights / Parameters

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Fine-Tuning vs Prompt Engg.

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RAG

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Agents

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Prompt Injection

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AI vs AGI

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Sampling Parameters

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Temperature

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Top-K

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Top-P

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Output Control

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Max Tokens

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Stop Sequences

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Repetition Penalties

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Frequency Penalty

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Presence Penalty

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Structured Outputs

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Zero-Shot Prompting

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One-Shot / Few-Shot Prompting

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System Prompting

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Role Prompting

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Contextual Prompting

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Step-back Prompting

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Chain of Thought (CoT) Prompting

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Self-Consistency Prompting

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Tree of Thoughts (ToT) Prompting

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ReAct Prompting

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Automatic Prompt Engineering

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AI Red Teaming

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Prompt Debiasing

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Prompt Ensembling

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LLM Self Evaluation

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Calibrating LLMs

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Prompt Tuning

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