Prompt Engineering — Get 10x Better Outputs

The same model gives mediocre or excellent output depending entirely on the prompt. These techniques are the difference — and they are a genuine, hireable skill in 2026.

1. Be specific and give a role

❌ "Write about databases."
✅ "You are a senior backend engineer. Explain database indexing to a
    2nd-year CS student in 150 words, with one analogy and one code example."

2. Few-shot — show, don't just tell

Give 2–3 examples of the input→output you want. The model pattern-matches your format instantly.

Classify sentiment.
"Loved it!" → positive
"Waste of money" → negative
"It was okay" → neutral
"Absolutely brilliant" →         # model returns: positive

3. Chain-of-thought — make it reason

For logic/math, add "Think step by step before answering." Forcing intermediate steps dramatically improves correctness — the model works through the problem instead of blurting a guess.

4. Force structured output

"Return ONLY valid JSON:
{ "name": string, "skills": string[], "years": number }
Resume: <paste text>"
# now you can parse the response reliably in code.

5. Give it an out

Add "If the answer isn't in the provided text, say 'I don't know'." This is the single most effective anti-hallucination line for real apps.