Prompt Engineering

Prompt Engineering Concept

Effective prompts is key to getting high-quality outputs from LLMs.
Strong prompts save time, reduce errors, turns a general model into specialized.
It's an iterative process of refining prompts based on model output.
Think of it like a recipe — good ingredients (context) and clear steps (instructions) yield reliable results.

Action Verbs: Start with direct verbs like Write, Explain, Summarize, Extract, Classify. Avoid vague verbs like Understand, Think, Try.
Specific: Mention exact requirements, constraints, tone, length, target audience, output format.
Context: Provide relevant background information, examples to guide model.
Delimiters: Use clear delimiters to separate instructions and data.
Iterate: Refine prompts based on output quality and errors.

Prompt Examples and their Effects:

Weak Prompt	Improved Prompt
Write a summary of this article.	Write a concise summary of the following article in 3 sentences, highlighting the main points and key takeaways.
Write a poem on rain.	Write a poem on topic rain in two paragraph for student of grade two.
What are the benefits of exercise?	List 5 scientifically proven benefits of regular exercise, including physical and mental health effects.
Translate this text to Spanish.	Translate the English text inside triple backticks to Spanish, ensuring that the tone is formal and the meaning is preserved: ```Hello, how are you?```
five must watch action movies?	Create a two-column table of 5 must-watch action movies released in the last decade with headings "Movie Title" and "Release Year" (no extra commentary).
Generate title for given text.	Generate a catchy and SEO-friendly title for the following blog post content inside triple backticks. Ouput has to be in json form as {"title": "generated_title"}. Input text: ```your_text```

Examples of weak prompts and their improved versions with specific instructions and context.

Conditional Rules: Add simple logic to keep outputs on track. language checks, keyword checks.
Role-playing: Assign a persona to control tone and content. financial analyst, support agent etc.

Example:

You will be given text between triple backticks. If it's in English, suggest a title. Otherwise, reply: 'I only understand English.' ``{}``
Act as a gentle customer support agent. If the question is not about our products, reply: 'I can help with product questions only.' Now answer: ``What app features do you offer?``.

To teach the model our desired format, reasoning style, constraints by giving exmples.
Make examples short, diverse and representative of edge cases.
One-shot: Provide one example.
Few-shot: Provide 2-5 examples to improve consistency and quality.

Example:

One-shot: Q: Sum the numbers 3, 5, and 6. A: 14 Q: Sum the numbers 2, 4, and 7. A:
Few-shot: Text: Today the weather is fantastic => Classification: positive Text: The furniture is small => Classification: neutral Text: I don't like your attitude => Classification: negative Text: That shot selection was awful => Classification:

Break down complex tasks into smaller steps to guide the model through the reasoning process.
Use explicit instructions to perform each step sequentially.
This can improve accuracy for tasks that require logical reasoning or multi-step calculations.

Example:

Step 1: Identify the main topic of the input text. Step 2: Determine the sentiment associated with the topic. Step 3: Output the classification in JSON format.

Extract 'Product Name', 'Display Size', 'Camera Resolution' from text inside triple quotes. Return result in json as: {"product_name": "", "display_size": "", "camera_resolution": ""}. Text: ``The Samsung Galaxy S23 features a 6.1-inch Dynamic AMOLED display and a 50 MP main camera for high-quality photography.``
Extract all nepali phone numbers and email addresses from the text inside triple ticks. Return the result in JSON format as: {"phone_numbers": [], "emails": []}. Text: ``Ram Sharma from Kathmandu asked me to email the report to [email protected]. If anything is unclear, he said I can call him at 9841234567. Later I spoke with Sita Gurung about the meeting. She told me to confirm the details at [email protected] or reach her on +977-9851023344. For finance matters, Bikash Adhikari said the quickest way is the office landline +977-1-4789123.``

Prompt Engineering Principles at a Glance:

Type	What it does	Best for	Example
Zero-shot	No examples, just instructions.	Fast general tasks.	Summarize text inside triple ticks in 3 bullets
One/Few-shot	Include 1 or more examples.	Style, format, reasoning consistency.	Q: … A: … Now answer: …
Role Playing	Assign a persona.	Tone and domain control.	Act as a calm customer support agent…
Conditional	Add conditional rules.	Guardrails, Routing	... If text is not English, reply 'I only understand English'.
Multistep Reasoning	Break down complex tasks into steps.	Complex / Sequential tasks.	Step 1: ... Step 2: ... Step 3: ...

Setup Gemini API Access

Sample Python Code to Call Gemini API

from google import genai API_KEY = "your-api-key-here" client = genai.Client(api_key=API_KEY) user_input = "Write a short poem about AI in 3 lines." response = client.models.generate_content(model="gemini-2.5-flash", contents=user_input) print(response.text)

System Prompt: Set context, rules, tone, output format, guideline.
User Prompt: The actual input from the end-user.
Assistant: The model's response based on the system and user prompts.
Some System instructions: - You are a helpful assistant. - Answer in a formal tone. - Provide concise responses. - Only use information from the provided text.

Examples:

system_instruction = "You are a language translator who translates English into Nepali Language. If you receive input other than English reply content has to be 'Not an English Text' else the converted one. Provide output in JSON as {'Input': user_word, 'Output': converted_word'}" user_input = "Hello, How are you doing?" response = client.models.generate_content(model="gemini-2.5-flash", config={'system_instruction': system_instruction}, contents=user_input) print(response.text)