Once you're past basic prompting, a handful of named techniques reliably lift the quality of LLM output. These five are the ones worth internalizing — each solves a specific class of problem.
1. Few-Shot Examples
Instead of describing the output you want, show two or three examples. The model matches the pattern far more reliably than it follows instructions:
Input: "The food was cold." → Sentiment: Negative
Input: "Loved every bite!" → Sentiment: Positive
Input: "It was fine." → Sentiment:
Few-shot is often the single highest-leverage change you can make.
2. Chain-of-Thought
For reasoning tasks, asking the model to work step by step before answering improves accuracy dramatically. It gives the model room to "think" instead of guessing an answer immediately:
Solve this step by step, then give the final answer.
3. Give It a Role
Assigning a persona focuses the model's knowledge and tone. "You are a security auditor reviewing this code" produces sharper, more relevant output than an unframed request.
4. Structured Output Instructions
When you'll parse the result programmatically, ask for a strict format and the model complies well:
Respond with valid JSON only:
{ "sentiment": "positive|negative|neutral", "confidence": 0.0-1.0 }
This turns free text into something your code can consume directly.
5. Decomposition
For complex tasks, break the work into a sequence of smaller prompts instead of one giant one. Summarize, then extract, then format — each step is easier to get right and easier to debug than a single prompt trying to do everything.
Summary
Few-shot examples teach by demonstration, chain-of-thought unlocks reasoning, a role sharpens focus, structured-output instructions make results machine-readable, and decomposition tames complex tasks. None of these are tricks — they are how the same model produces reliably better output when you ask the right way. Reach for the one that matches your problem and your results jump without changing models at all.