Learn to communicate with AI like a pro ๐
Prompt engineering is the art and science of crafting instructions to communicate with AI models effectively. It's like learning to speak the AI's language!
Good prompts = Better results. It's the difference between asking a question clearly vs. confusing someone with unclear instructions.
You give clear instructions โ AI understands โ AI gives you exactly what you want. Simple as that!
It's about writing clear, specific instructions that tell the AI exactly what to do.
Imagine telling a friend to make tea. If you say "Make tea," they might be confused. But if you say "Boil water for 5 minutes, add tea bag, let it steep for 3 minutes, add milk," they'll do it perfectly!
โ Bad: "Write something about cats"
โ Good: "Write a 100-word paragraph about why cats make good pets, using simple language for kids aged 8-10"
Providing background information so the AI understands the situation better.
If you tell someone "I'm tired," they might suggest coffee. But if you say "I'm tired because I worked 12 hours," they'll suggest rest instead. Context changes the response!
โ Without context: "Write an email"
โ With context: "Write a professional email to my boss requesting a day off next Friday. I've been working on a big project and need to rest."
Telling the AI what role or expertise it should take on.
If you ask a doctor vs. a chef about food, you'll get different answers. The doctor talks about nutrition, the chef talks about taste. Roles matter!
โ No role: "Explain quantum physics"
โ With role: "You are a physics teacher explaining to 10-year-olds. Explain quantum physics using everyday examples."
Clearly defining what you're giving the AI (input) and what you want back (output).
Like a vending machine: You put money in (input) and get snacks out (output). Clear exchange!
Input: A list of 5 products with prices
Output: A table showing which product is cheapest
Setting rules and limits for how the AI should respond.
Like rules in a game. Without rules, the game is chaos. With rules, everyone knows what to do!
Constraints:
- Use simple language (no technical jargon)
- Maximum 100 words
- Friendly and encouraging tone
- Include 2 examples
Using special symbols to clearly separate different parts of your prompt.
Like using folders to organize files. Instead of mixing everything, you put each thing in its own folder!
Using delimiters:
---TASK---
Summarize this text
---TEXT---
[Your text here]
---FORMAT---
3 bullet points
What is it? Asking the AI to do something without giving any examples.
Analogy: Like asking someone to cook a dish without showing them a recipe first. They use their general knowledge!
Example:
Prompt: "Translate 'Hello' to Spanish"
AI: "Hola"
When to use: Simple tasks, general knowledge questions
Pros: Quick, no examples needed
Cons: May not work for complex tasks
What is it? Giving 2-3 examples before asking the AI to do the task.
Analogy: Like showing someone 2-3 examples of how to fold paper before asking them to fold a new one.
Example:
Example 1: "Happy" โ Positive
Example 2: "Sad" โ Negative
Now classify: "Excited"
AI: "Positive"
When to use: Complex tasks, specific patterns needed
Pros: Better accuracy, AI learns from examples
Cons: Need to create examples
What is it? Asking the AI to show its thinking step-by-step.
Analogy: Like asking someone to explain their math solution step-by-step instead of just giving the answer.
Example:
Prompt: "If I have 5 apples and buy 3 more, then give 2 to my friend, how many do I have? Think step-by-step."
AI: "Step 1: Start with 5 apples. Step 2: Buy 3 more = 5+3=8. Step 3: Give 2 away = 8-2=6. Answer: 6 apples"
When to use: Complex math, reasoning tasks
Pros: Better accuracy, easier to debug
Cons: Takes longer
What is it? Asking the AI the same question multiple times and finding the most common answer.
Analogy: Like asking 5 people the same question and going with the answer most people give.
Example:
Ask same question 3 times โ Get 3 answers โ Pick the most common one
When to use: When you need very accurate answers
Pros: Very accurate
Cons: Expensive, takes time
What is it? Giving the AI relevant documents/information before asking a question.
Analogy: Like giving someone a textbook before asking them a question about the topic.
Example:
[Provide: Wikipedia article about Python]
Prompt: "Based on the article, what is Python?"
When to use: Specific domain knowledge needed
Pros: More accurate, uses real data
Cons: Complex to set up
What is it? AI reasons about what to do, then takes action, then observes the result.
Analogy: Like a detective: Think about what to do โ Do it โ See what happens โ Think again.
Example:
Thought: "I need to search for current weather"
Action: "Search for weather in Delhi"
Observation: "It's 35ยฐC and sunny"
Thought: "Now I can answer the question"
When to use: Complex tasks needing tools/actions
Pros: Can use external tools
Cons: Very complex
Learn by building! Each project starts from scratch.
What you'll build: An app that reads text and tells you if it's positive, negative, or neutral.
Real-world use: Companies use this to analyze customer reviews, social media posts, and feedback.
Concepts used: Zero-shot prompting, Role assignment, Constraints
Time needed: 30 minutes
What you'll build: Extract specific information (names, emails, dates) from messy text.
Real-world use: Businesses use this to extract data from invoices, emails, and documents.
Concepts used: Few-shot prompting, Context setting, Input-output framing
Time needed: 45 minutes
What you'll build: A chatbot that remembers context and gives helpful responses.
Real-world use: Customer service, FAQ bots, personal assistants.
Concepts used: Chain-of-thought, Role assignment, Memory management
Time needed: 60 minutes
Prepare for your interviews with these common questions!
Key terms you need to know
Definition:
A prompt is the input text or instruction you give to an AI model to generate a response.
Definition:
A Large Language Model is an AI system trained on massive amounts of text data to understand and generate human-like text.
Definition:
Temperature is a parameter that controls the randomness and creativity of AI responses. Lower = more focused, Higher = more creative.
Definition:
Tokens are pieces of text that the AI processes. Roughly, 1 token โ 4 characters or 0.75 words in English.
Definition:
When an AI generates false or made-up information that sounds plausible but isn't true.
Definition:
The maximum amount of text (in tokens) that an AI can process at once, including both input and output.
Definition:
The process of further training a pre-trained model on specific data to specialize it for particular tasks.
Definition:
Numerical representations of text that capture semantic meaning, allowing AI to understand relationships between words.
Definition:
The ability of an AI to perform tasks it wasn't explicitly trained on, without any examples.
Definition:
Learning to perform a task from just a few examples provided in the prompt.
Definition:
A prompting technique where the AI shows its reasoning process step-by-step.
Definition:
A technique that combines information retrieval with text generation by providing relevant documents to the AI.