Prompt Engineering Introduction

Introduction

Prompt engineering is the craft of writing inputs that consistently get good outputs from an LLM. It's the cheapest, fastest way to improve an AI feature — no model training required.

Beginner analogy: A prompt is like a brief to a very smart, very literal contractor. Vague brief → vague result. Specific brief with examples → great result.

Understanding the topic

Core concepts to understand:

• Good prompts are specific, contextual, and structured.
• Always tell the model: role, task, format, constraints.
• Provide examples (few-shot) for tricky tasks.
• Iterate — prompt v1 is rarely the final version.
• Evaluate with a fixed test set, not vibes.

Syntax reference

Visual workflow / architecture:

Bad prompt:    "Summarise this."
                            │
                            ▼  inconsistent length, style
Good prompt:
  ROLE:        "You are a senior editor."
  TASK:        "Summarise the article below."
  FORMAT:      "3 bullet points, max 15 words each."
  CONSTRAINTS: "Plain English, no jargon."
  INPUT:       <article text>
                            │
                            ▼  consistent, on-brand

Real-world use

Companies like Anthropic publish entire prompt-engineering guides. Frameworks like LangChain ship prompt templates. Job titles like 'Prompt Engineer' command $300k+ at top AI firms.

Best practices

• Always include a clear role and task.
• Specify the output format (JSON, bullets, markdown).
• Add 1–3 examples for non-trivial tasks (few-shot).
• Test prompts on edge cases before shipping.

Common mistakes

• Treating prompts as one-shot — always iterate.
• Burying instructions inside long context where the model misses them.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What makes a good prompt?
• Q2. Why do we add examples to prompts?
• Q3. How do you evaluate prompt quality?