Prompt engineering is the practice of carefully crafting input text to guide a large language model's (LLM's) behavior. It uses the model's pre-existing capabilities without any additional weight updates.
Prompt engineering is "far faster" and "more resource-friendly" than fine-tuning, while preserving the model's broad knowledge.
1. Master Zero-Shot and Few-Shot Prompting
In zero-shot prompting, the user provides only the task instruction or question (with no examples):
Classify the sentiment of the following review: "I absolutely love this product!" Sentiment:In few-shot prompting, the user provides a small set of example input–output pairs (often 3–10) illustrating the desired behavior:
Classify the sentiment of the following reviews:
Review: "The product broke after one use." Sentiment: Negative
Review: "Excellent quality and fast shipping." Sentiment: Positive
Review: "Great value for the price." Sentiment:2. Chain-of-Thought (CoT) Prompting
To create multi-step reasoning, we can prompt the model to think aloud by generating intermediate steps. Even a simple zero-shot phrase like "Let's think step by step" can encourage the model to output a reasoning chain before answering:
Question: If a train travels at 60 miles per hour for 2.5 hours, how far does it travel?
Let's think step by step:3. Assign a Role with System or Role Prompts
Defining a Persona or system-level instruction often results in more coherent and task-focused output:
You are a cybersecurity expert. List the top 3 OWASP API security risks in bullet points.Or in OpenAI, you can assign the system prompts:
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "Assistant is a large language model trained by OpenAI."},
{"role": "user", "content": "Who were the founders of Microsoft?"}
]
)4. Enforce Structured Output Formatting
We can instruct the model to format its output in a specific way, which is crucial for downstream parsing:
Text: "John Doe, 35, software engineer in NY, joined in 2015."
Format as JSON:5. Prompt Chaining
For multi-step tasks that might exceed a single prompt's scope, we can chain prompts together:
- Prompt one might extract key facts
- Prompt two analyzes those facts
- Prompt three formats the final report
An advanced variant is a self-correction chain: after generating an answer, you feed it back to the model with a prompt like "Review your answer and fix any errors."
6. Apply Psychological and Linguistic Priming
Psychological and linguistic priming involves using specific language cues or context-setting phrases to shape the style, tone, and internal context:
You are a Chief Information Security Officer (CISO) at a fintech startup. Provide a risk assessment for storing customer data in an on-premises database with an academic tone suitable for a scientific paper to a high school student with no background in data science.7. Compare and Iterate Across Multiple LLMs
No single LLM is perfect for every task. Different models like GPT-4, Claude, Gemini, or open-source LLaMA have different strengths, quirks, and failure modes.
Comparing their outputs with the same prompts helps you:
- Find the best fit for your specific use case
- Identify gaps or biases in one model's response
- Spot hallucinations or errors faster
By iterating across models, we can build a composite view of what's possible and refine our final prompt for the best outcome.