AI Concepts

Prompt Engineering

Master the art and science of crafting effective prompts for large language models. Learn techniques like few-shot, chain-of-thought, and system prompts to get the best results from any AI model.

What is Prompt Engineering?

Prompt Engineering is the practice of designing and optimizing inputs (prompts) to large language models to elicit the most accurate, relevant, and useful outputs. It is the primary interface between humans and AI models, and mastering it can dramatically improve AI performance without any code changes or model fine-tuning.

A well-crafted prompt can turn a mediocre response into an excellent one. The difference between "Write about dogs" and a structured prompt with role, context, format, and constraints can be the difference between a generic paragraph and a professional-quality article.

Core Techniques

🎯 Zero-Shot

Direct instruction without examples. Works for simple, well-defined tasks.

📝 Few-Shot

Provide 2-5 examples of desired input/output pairs to guide the model's behavior.

🔗 Chain-of-Thought

Ask the model to "think step by step" — dramatically improves reasoning accuracy.

🏗️ System Prompts

Set the model's role, personality, rules, and constraints before the conversation begins.

Prompt Templates

The RACE Framework

Role: You are a [specific expert role]
Action: [What you want the AI to do]
Context: [Background information and constraints]
Expected Output: [Format, length, style of response]

Chain-of-Thought Example

Solve this problem step by step:

A store has 45 apples. They sell 12 in the morning and receive
a shipment of 30 in the afternoon. Then they sell 18 more.
How many apples remain?

Think through each step before giving the final answer.

Advanced Techniques

Prompt Chaining: Break complex tasks into a sequence of simpler prompts, feeding each output into the next.
Tree of Thought: Explore multiple reasoning paths and select the best one.
Self-Consistency: Generate multiple responses and pick the most common answer.
ReAct Prompting: Interleave reasoning and action steps for tool-using agents.
Least-to-Most: Decompose into sub-problems, solve from simplest to hardest.

Best Practices

Be Specific: Vague prompts get vague answers. Include format, length, tone, and constraints.
Provide Context: Give the model all the background information it needs.
Use Delimiters: Separate instructions from content using triple backticks, XML tags, or markdown.
Iterate: Prompt engineering is iterative — test, evaluate, and refine.
Control Output Format: Request JSON, markdown, tables, or bullet points explicitly.