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Prompt Engineering
75 min
Beginner
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Prompt Engineering for Beginners: From Zero‑Shot to Few‑Shot and Reasoning

Prompt engineering is like learning to talk to a very smart, very literal coworker who never sleeps. With a few clear instructions, you get magic; with vague notes, you get chaos. In this beginner-friendly tour, you’ll learn how to write prompts that LLMs actually follow. You’ll see when to use zero‑shot (no examples) versus few‑shot (a couple of good examples), and how to handle multi‑step thinking with chain‑of‑thought techniques. We’ll lean on research (Brown et al., 2020; Min et al., 2022) and you’ll get hands-on exercises you can run in any LLM playground.

There’s a lightweight workflow too: test, tweak, and track your prompts without drowning in tabs. I’ll point you to friendly community hubs like the Prompt Engineering Guide (promptingguide.ai) and DAIR.AI courses. By the end, you’ll have a tiny but mighty prompt library for common tasks and a simple process to keep improving it. Bonus: less time yelling at your screen, more time shipping useful results.

Robort Gabriel

Robort Gabriel

Full Stack Developer, SEO Expert, Website Manager & Content Creator.

Jan 8
Prompt Engineering for Beginners: From Zero‑Shot to Few‑Shot and Reasoning

Prerequisites

  • •A computer with internet and a modern browser
  • •Access to an LLM playground (e.g., OpenAI, Anthropic, or similar). Make a free account.
  • •Optional: API access with a small budget to run prompts programmatically
  • •Basic comfort reading JSON and copying/pasting templates into a playground
  • •Optional (for local docs): Node.js >= 18 and pnpm installed to run the Prompt Engineering Guide locally
  • •A simple spreadsheet or notes app to track A/B test results and example datasets

What You'll Learn

  • ✓Explain what prompt engineering is and tell the difference between zero‑shot and few‑shot prompting in your own words.
  • ✓Write clear, constrained prompts (role, task, constraints, input, output format) and score them with a simple rubric.
  • ✓Build few‑shot prompts with solid examples, consistent formats, and balanced labels; measure accuracy on a 10, 20 item test set.
  • ✓Use chain‑of‑thought and self‑consistency sampling for multi‑step reasoning; compare results against simple baselines.
  • ✓Create and iterate prompt templates; run basic A/B tests; tune temperature/top_p; log prompts and outputs so results are reproducible.
  • ✓Run the Prompt Engineering Guide locally with Node >= 18 and pnpm; use it to research techniques and tools.
  • ✓Assemble a small prompt library (classification, structured extraction, Q&A) with clear notes and evaluation criteria.

Course Contents

5 chapters • 75 min total length

1.Foundations of Prompt Engineering: Clarity, Structure, and Zero‑Shot
12m
2.Few‑Shot Prompting for Prompt Engineering: Formats, Labels, and Examples
18m
3.Reasoning Prompts: Chain‑of‑Thought and Self‑Consistency
15m
4.Prompt Engineering Tools and Evaluation: Templates, Guardrails, and A/B Tests
12m
5.Build a Beginner Prompt Library: Classification, Extraction, and Q&A
15m

Frequently Asked Questions

Prompt engineering is the art of writing instructions that LLMs can follow without guessing. It boosts accuracy, safety, and consistency. Beginners should learn it because tiny tweaks, clearer tasks, tighter constraints, or a couple of good examples, can deliver big gains without code or fine-tuning. You’ll quickly prototype Q&A, classification, and reasoning tasks and look suspiciously productive.
No. You can complete everything in a web-based LLM playground. Optional steps show how to run the Prompt Engineering Guide locally (Node >= 18 and pnpm) and how to log results. Knowing a bit of JSON helps, but you can follow along without it.
Few‑shot prompting adds a few in-context examples at runtime, fast and flexible, no training needed. Fine-tuning updates model weights with many labeled examples, slower, costlier, but often better for deep domain behavior. Start with few‑shot; move to fine‑tuning when you need persistent behavior across tasks, tighter privacy, or you keep pasting the same examples every day.
Use chain‑of‑thought for problems that need multi‑step reasoning (math word problems, planning, transformations). Compare it to zero‑/few‑shot baselines and try self‑consistency by sampling multiple rationales. For production, keep rationales concise or structured to control cost and avoid bloated outputs.
Plan about 75 minutes. Chapters are 10, 20 minutes each with hands-on exercises. It starts friendly and builds up, from zero‑shot, to few‑shot, to reasoning, without frying your brain.