Why Prompt Engineering Matters
LLMs are completion engines — they predict the most likely continuation of your input. The framing, structure, and examples you provide dramatically shape what "most likely" means. Master prompting and you unlock capabilities the model always had but couldn't express with a vague input.
Core Techniques
Zero-Shot Prompting
Simply instruct the model without examples. Works well for well-understood tasks. Tip: be specific about the format you want.
Classify the following customer review as POSITIVE, NEGATIVE, or NEUTRAL.
Return only the label, nothing else.
Review: "The product arrived damaged but customer service resolved it quickly."Few-Shot Prompting
Provide 2–5 examples of input → output pairs before your actual query. The model infers the pattern:
Translate English to SQL:
English: Show all users who signed up last month.
SQL: SELECT * FROM users WHERE created_at >= NOW() - INTERVAL '1 month';
English: Find the top 5 products by revenue.
SQL:Chain-of-Thought (CoT)
Adding "Let's think step by step." to a prompt causes the model to reason through a problem before answering. This dramatically improves accuracy on math, logic, and multi-step tasks:
On the GSM8K math benchmark, CoT prompting improved GPT-3's accuracy from 17% to 58%. Zero cost; just add four words.
Advanced Techniques
System Prompt Architecture
Structure your system prompt with clear sections: role, context, constraints, output format, and examples. A well-structured system prompt is worth more than a complex user prompt.
XML Tags for Structure
Claude and many modern models respond well to XML-style delimiters for separating sections:
<task>Summarise the following article in 3 bullet points</task>
<article>{{article_content}}</article>
<format>• Bullet 1
• Bullet 2
• Bullet 3</format>Constrained Output
Ask for JSON, CSV, or a specific schema and validate with Pydantic. Structured outputs from OpenAI and Anthropic guarantee schema adherence without post-processing.
Other Articles
