You don't need to train a model to get dramatically better results. You just need to talk to it better. Prompt engineering is the practice of crafting inputs that reliably produce high-quality outputs.
**The most powerful techniques:**
**1. Chain-of-Thought (CoT)**
Add 'Think step by step' to any reasoning task. This forces the model to show its work and dramatically improves accuracy on math, logic, and multi-step problems.
**2. Few-Shot Examples**
Show the model 2-3 examples of input → output pairs before your actual request. The model pattern-matches and follows the format precisely.
**3. Role Assignment**
'You are an expert in X' shifts the model's style, vocabulary, and approach. 'You are a senior software engineer reviewing code for security vulnerabilities' gets better security reviews than just 'Review this code.'
**4. Output Formatting**
Specify exactly what you want: 'Respond in JSON with keys: title, summary, tags.' Structured outputs are far more reliable.
**5. Decomposition**
Break complex asks into sub-problems. Don't ask 'Write a full business plan.' Ask for the market analysis first, then the financial model, then the go-to-market strategy.
**6. Self-Critique**
'Review your response and identify any errors or gaps. Then provide an improved version.' Models can catch their own mistakes when prompted to.
Good prompts are specific, give context, set constraints, and specify format. Bad prompts are vague. The model does what you ask — not what you meant.
**Key takeaway:** 'Think step by step' + role + examples + format = dramatically better AI outputs. This is learnable.