Best Practices
Practical guidelines for using AI effectively, safely, and responsibly.
Quick Checklist
Prompt Engineering
Learn moreBe Specific and Explicit
Instead of "summarize this", say "summarize this in 3 bullet points focusing on key findings".
Write a 200-word explanation of transformers for a college freshman studying CS.
Explain transformers.
Use System Prompts Wisely
Set context and constraints at the beginning to guide model behavior throughout.
You are an expert Python developer. Always include error handling and type hints.
Be helpful.
Chain-of-Thought Prompting
Ask the model to think step-by-step for complex reasoning tasks.
Let's solve this step by step. First, identify the variables...
What's the answer?
Provide Examples (Few-Shot)
Show the model what good outputs look like before asking for new ones.
Example: Input: "happy" → Output: "joyful, elated, pleased". Now do: "sad"
Give me synonyms.
Safe & Responsible Use
Learn moreVerify Critical Information
AI can hallucinate facts, especially dates, statistics, and technical details.
Cross-reference AI outputs with primary sources for important claims.
Blindly trust AI for medical, legal, or financial advice.
Understand Limitations
Know what AI is good at (pattern matching, synthesis) and bad at (novel reasoning, current events).
Use AI as a starting point or brainstorming partner.
Expect AI to replace domain experts or make final decisions.
Protect Privacy
Don't share sensitive personal data, trade secrets, or confidential information with AI systems.
Anonymize data before using AI for analysis.
Paste full customer records or proprietary code into public AI tools.
Consider Bias
AI models reflect biases in training data. Review outputs critically.
Ask for multiple perspectives and test with diverse inputs.
Use AI outputs for hiring or evaluation without human review.
Working with Code
Learn moreReview Generated Code
AI code often works but may have security flaws, inefficiencies, or subtle bugs.
Test AI-generated code thoroughly; understand what it does before using.
Copy-paste code directly into production without review.
Provide Full Context
Share relevant code, error messages, and expected behavior.
Here's my function [code], the error [error], and I expect [behavior].
"My code doesn't work, fix it."
Use for Learning, Not Replacing
AI is best as a teaching tool to understand concepts, not just get answers.
Ask "explain why this approach works" or "what are alternatives?"
Ask for code without trying to understand the solution.
Iterate and Refine
Treat AI as a collaborative partner—refine outputs through conversation.
"Good start, but can you add error handling for edge case X?"
Accept the first output as final.
Building with AI
Learn moreStart with RAG
Retrieval-Augmented Generation grounds AI in your data, reducing hallucinations.
Index your knowledge base and retrieve relevant context before generation.
Rely solely on fine-tuning for domain-specific knowledge.
Implement Guardrails
Add validation layers to catch inappropriate or incorrect outputs.
Use content filters, output validators, and human review for critical paths.
Deploy AI directly to users without safety checks.
Monitor and Evaluate
Track AI performance over time with real user interactions.
Log inputs/outputs, measure quality metrics, gather user feedback.
Deploy and forget—AI behavior can drift or degrade.
Design for Failure
AI will sometimes fail. Build graceful fallbacks.
Have human escalation paths and honest "I don't know" responses.
Promise 100% accuracy or hide AI limitations from users.
The Core Philosophy
AI is a powerful tool, not magic. Treat it like a brilliant but occasionally confused intern: it can do amazing work, but needs clear instructions, careful supervision, and fact-checking. The best results come from human-AI collaboration, not blind automation.