What's in this lesson: Core techniques and pitfalls of prompt engineering, plus a short assessment.
Why this matters: Better prompts mean better model outputs—essential for any AI application.
Why this matters: Better prompts mean better model outputs—essential for any AI application.
What Is Prompt Engineering?
Prompt engineering is the practice of designing inputs (prompts) to get the best possible output from a large language model (LLM).
- A well-crafted prompt can turn a generic model into a domain expert.
- Small wording changes can dramatically change the quality of the output.
Key takeaway: The model is only as good as the instructions you give it.
Core Techniques
There are three fundamental prompt techniques you should know:
- Zero-shot: Ask the model directly with no examples.
- Few-shot: Provide 2-3 examples before your actual question.
- Chain-of-thought: Ask the model to "think step by step."
Few-shot prompting often works best when you need a specific output format.
Common Pitfalls
Avoid these mistakes when writing prompts:
- Being too vague — "Write something about dogs" vs. "Write a 200-word blog intro about golden retriever training tips."
- Overloading context — Dumping an entire document when a summary would suffice.
- Ignoring output format — Always specify JSON, bullet points, etc. when you need structured output.
Rule of thumb: If a human couldn't follow your prompt, neither can the model.
Assessment — Question 1
Which prompting technique involves providing the model with 2-3 examples before the actual question?
Assessment — Question 2
What is the main benefit of asking a model to "think step by step"?
Assessment — Question 3
Which of the following is a common prompting pitfall?
Your Results
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