Prompt Engineering Playbook
Techniques for optimizing system prompts, few-shot examples, and chain-of-thought reasoning.
Prompt Basics
What Is Prompt Engineering?
Learn what prompt engineering is and why it matters. Discover how crafting clear instructions helps you unlock the full potential of AI models like ChatGPT, Gemini and Anthropic
Large Language Model (LLM)
A friendly, practical guide to large language models—what they are, how they work at a high level, and why they matter for both technical and non-technical roles.
LLM Parameters
Learn the key LLM parameters (temperature, top_p, max tokens, and more) and how to tune them for better answers—whether you’re writing code, sales emails, or support replies.
Writing a Great Prompt
A practical prompt framework you can use anywhere—engineering, sales, marketing, HR, support—to get clearer, more reliable results from LLMs.
AI Providers & Models
A practical guide for teams choosing between OpenAI, Anthropic Claude, Google Gemini, and Meta Llama—based on speed, reasoning, context length, deployment needs, and cost.
Core Techniques
Zero-Shot vs Few-Shot
A practical guide to zero-shot and few-shot prompting—what they are, when to use each, and how examples improve consistency for both technical and non-technical teams.
Step-by-Step Prompting
Learn step-by-step prompting (decomposition) to improve accuracy, reduce hallucinations, and produce consistent outputs for both technical and non-technical workflows.
Role (Persona) Prompting
Learn role prompting (persona prompting) to get more accurate, on-tone, and structured outputs—whether you need a senior engineer, a sales coach, or an HR partner.
Formatting LLM Outputs
Learn how to prompt LLMs to output clean tables, bullet lists, and Markdown for reliable docs, reports, tickets, and engineering workflows.
Reasoning & Logic
Chain-of-Thought (CoT)
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Zero-Shot Chain-of-Thought
A practical guide to zero-shot Chain-of-Thought prompting—when “let’s think step by step” helps, when it doesn’t, and safer alternatives that improve accuracy without long reasoning dumps.
Self-Consistency
Learn self-consistency prompting—how sampling multiple LLM answers and voting/synthesizing improves reliability for reasoning, classification, and high-stakes decisions.
Agent Prompting
Prompt Chaining
Learn how to break complex work into reliable AI steps using prompt chaining. Includes practical chains for engineers and non-technical teams.
ReAct Prompting
Learn ReAct prompting—Reason + Act—to make AI more reliable when solving problems, using tools, and validating answers across technical and non-technical workflows.
Program-Aided Language
Learn Program-Aided Language (PAL): a prompting style where the model writes small programs to compute answers more reliably, with examples for engineers and business teams.
Tree of Thoughts
Learn Tree of Thoughts (ToT): a prompting technique that explores multiple solution paths, evaluates them, and converges on the best answer—great for hard decisions and complex problems.
Reflexion Loops
Learn Reflexion: an automated self-correction loop where an LLM critiques its output, updates a memory of mistakes, and retries—useful for agents, writing, and high-stakes work.
Applying AI Skills
Email Prompts
Learn practical prompt templates for writing great emails with AI—fast, professional, and on-brand—for both technical and non-technical roles.
Data Analysis
Learn prompt patterns for reliable data analysis with AI: framing questions, structuring outputs, using assumptions, and adding verification—plus examples for engineers and business teams.
Code Generation
Learn how to prompt AI to generate code you can ship: clear specs, constraints, tests, and review loops—plus examples for engineers and non-technical teams.
AI Safety
Prompt Hacking
Understand prompt injection and jailbreaking: how attacks work, why they succeed, and practical defenses for building safer AI systems in the real world.
Stop Hallucinations
Learn how to spot LLM hallucinations early and reduce them with better prompts, verification loops, and practical guardrails for both technical and non-technical work.
Prompt Evaluation
Learn how to test prompts like a product: define success criteria, build a small eval set, score outputs, and iterate with confidence—without guessing.