What Is Prompt Engineering?
Prompt engineering is the art of crafting instructions that get the best possible output from LLMs. It's not about tricks — it's about clear communication with AI systems. In 2026, it's a core skill for every developer working with AI.
Technique 1: Few-Shot Learning
Provide examples of the input-output pattern you want. The model learns the pattern from your examples and applies it to new inputs.
Example:
Instead of: "Classify this text as positive or negative"
Use: "Classify the sentiment. Examples: 'I love this!' → positive. 'Terrible product' → negative. 'Absolutely amazing experience' → positive. Now classify: 'The worst service ever'"
Our Few-Shot Classification exercise lets you implement this pattern hands-on.
Technique 2: Chain-of-Thought (CoT)
Ask the model to think step-by-step. This dramatically improves performance on reasoning tasks — math, logic, code debugging.
Example: "Think through this step by step: If a train travels at 60mph for 2.5 hours, then 80mph for 1.5 hours, what's the total distance?"
The model breaks it down: 60×2.5 = 150 miles + 80×1.5 = 120 miles = 270 miles total.
Technique 3: Role Prompting
Assign the model a specific role or persona. This activates domain-specific knowledge and adjusts the response style.
Example: "You are a senior ML engineer reviewing code. Identify potential issues with this training pipeline..."
Technique 4: Structured Output
Request specific output formats to get clean, parseable results.
Example: "Analyze this text and return a JSON object with fields: sentiment (positive/negative/neutral), confidence (0-1), key_phrases (array of strings)"
Technique 5: Constraint Prompting
Set explicit boundaries on the response:
- "Answer in exactly 3 bullet points"
- "Use only information from the provided context"
- "If you're not sure, say 'I don't know' instead of guessing"
Technique 6: Self-Consistency
Generate multiple responses and pick the most common answer. This reduces errors on tasks where the model might give different answers on different attempts.
Common Mistakes
- Being too vague: "Write something about AI" vs "Write a 200-word summary of how transformers work, suitable for a CS student"
- Not providing context: Always include relevant background information
- Ignoring output format: If you need structured data, ask for it explicitly
- Prompt injection: Always validate and sanitize user input before including it in prompts
Practice These Techniques
Try our Few-Shot Classification and Build a Simple Chatbot exercises to practice prompt engineering patterns. For a deep dive, follow the LLM Engineer path which covers prompt engineering, RAG, and production LLM deployment.