Free preview · Lesson 2
Prompt Engineering
Once a model is in front of you, the quality of what you get back depends heavily on how you ask. Prompt engineering is the practice of structuring requests — instructions, examples, and format — so the model reliably produces what you need.
- Apply the core prompting patterns: clear instructions, few-shot, chain-of-thought, and structured output.
- Explain why prompting alone breaks down once work spans many turns, tools, and documents.
Once a model is in front of you, the quality of what you get back depends heavily on how you ask. Prompt engineering is the practice of structuring requests — instructions, examples, and format — so the model reliably produces what you need.
The techniques that matter
A few patterns recur across every serious guide:
Technique | What it does |
|---|---|
Clear instructions & role | State the task, audience, and output format explicitly |
Few-shot examples | Show 1–5 worked examples so the model imitates the pattern |
Chain-of-thought | Ask the model to reason step by step before answering |
Structured output | Request JSON, XML tags, or a fixed schema for downstream use |
Chain-of-thought prompting — first shown by Wei et al. (2022) — was a watershed: simply asking a model to "think step by step" markedly improved arithmetic and reasoning accuracy. The 2024 Prompt Report later catalogued dozens of such techniques into a single taxonomy.
Example. "Summarise this contract" is a weak prompt. "You are a paralegal. List the three obligations, two termination clauses, and any auto-renewal in a markdown table; quote the clause text" is a strong one — role, task, structure, and grounding in one breath.
Why it is not enough
Prompt engineering optimises a single message. As soon as work spans many turns, tools, and documents, the bottleneck moves from "the prompt" to "everything else in the window" — which is exactly why context engineering emerged. Prompting remains foundational, but it is the first technique, not the last.
References & further reading
- Wei et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in LLMs. NeurIPS.
- Kojima et al. (2022). Large Language Models are Zero-Shot Reasoners. NeurIPS.
- Schulhoff et al. (2024). The Prompt Report: A Systematic Survey of Prompting Techniques. arXiv.
- DAIR.AI (2024). Prompt Engineering Guide. promptingguide.ai.