Prompt design is one of the highest-leverage skills for LLMs. Few-shot examples teach by demonstration. Chain-of-thought reasoning makes models explain their thinking. Prompt chaining decomposes complex tasks. These techniques often outperform fine-tuning.
Few-Shot Learning
Show examples of the task, then ask for a new one.
Example 1: "Translate to French: Hello" → "Bonjour"
Example 2: "Translate to French: Goodbye" → "Au revoir" Now: "Translate to French: Water" → [Model predicts: "Eau"]
Few-shot is more effective than zero-shot because the model learns task structure from examples.
Chain-of-Thought (CoT)
Ask the model to reason step-by-step before answering.
"What is 47 × 3 + 15?
Let's think step by step:
- First, calculate 47 × 3 = 141
- Then add 15: 141 + 15 = 156
Answer: 156"
CoT improves accuracy on reasoning tasks by forcing the model to show its work.
Prompt Chaining
Break complex tasks into simpler sub-tasks.
Task: Analyze sentiment and suggest improvements for a customer review.
Step 1: Analyze sentiment Prompt: "What is the sentiment of this review? [review text]" Output: "Negative"
Step 2: Extract complaints Prompt: "List the complaints in this review. [review text]" Output: [list of complaints]
Step 3: Generate improvements Prompt: "How could the product address these complaints? [complaints]" Output: [suggestions]
Chaining breaks down complex reasoning into tractable steps.
Conclusion
Prompt engineering is a high-leverage skill. Few-shot examples, chain-of-thought, and chaining improve performance without expensive fine-tuning or model changes. Understanding prompting techniques is essential for effective LLM usage. Next: hosting and deploying LLMs in production.