Unlock Better AI Responses: 3 Valuable Prompting Techniques from Google's Whitepaper
Recently, I've been exploring ways to improve how we communicate with AI. Google published a helpful whitepaper on Prompt Engineering (check it out here: https://www.kaggle.com/whitepaper-prompt-engineering), and I wanted to share three techniques from it that I found particularly useful:
We're all talking to AI more and more, but sometimes the answers we get aren't quite right. The good news is that how we ask makes a big difference. Based on some deeper dives into prompt engineering (like Google's own guides!), here are three techniques you can use to get more accurate, creative, and useful results from your AI assistants:
1. Take a "Step Back" Before Diving In
The Idea: Instead of asking your main question directly, first ask the AI a more general or abstract question about the underlying concept or principle. Then, use the AI's answer to that general question as context when you ask your specific question.
Think of it like: Planning the structure of an essay before writing the first sentence. You figure out the main points first, then fill in the details.
How it Helps: This "step back" helps the AI activate broader knowledge and reasoning pathways. By considering the bigger picture first, it can often generate more insightful and accurate answers to your specific problem later.
Example: Instead of asking "Write a storyline for a challenging shooter game level set in a flooded lab," you might first ask, "What are 5 common themes or settings that make shooter game levels challenging and engaging?" (This first question helps the AI brainstorm foundational ideas before tackling the specific request.) Then, use one of those themes (like the underwater lab idea it might generate) in your final prompt: "Okay, using the 'Underwater Research Facility' theme, write a one-paragraph storyline..."
2. Use "Self-Consistency" for Tougher Reasoning
The Idea: When dealing with problems that require complex reasoning (like math word problems or tricky logic puzzles where AI might stumble), don't rely on just one answer. Run the exact same prompt multiple times (you might need to adjust settings like 'temperature' slightly higher to encourage different reasoning paths). Look at all the answers generated. The final answer that appears most frequently across the different runs is often the most reliable one.
Think of it like: Getting a second (and third, and fourth) opinion on a difficult diagnosis. If multiple experts independently arrive at the same conclusion, you can be more confident it's correct.
How it Helps: Large Language Models can sometimes make logical errors. By generating several different reasoning attempts and looking for the most common outcome (a process called majority voting, which you or the system usually does after getting the responses), you significantly increase the chance of landing on the correct final answer.
3. Give Clear Instructions, Not Just Limits (Instructions > Constraints)
The Idea: Tell the AI specifically what to do rather than just listing things not to do. Frame your requests positively.
Think of it like: Giving directions by saying "Turn left at the next light," instead of "Don't turn right, don't go straight." The first is much clearer and less prone to misunderstanding.
How it Helps: Positive instructions are generally easier for both humans and AI to follow. They clearly define the desired outcome. Relying too heavily on constraints ("don't do X, don't mention Y, avoid Z") can be confusing, potentially contradictory, and might unnecessarily limit the AI's creativity or ability to find the best solution within the allowed space. While constraints are sometimes needed (especially for safety or strict formatting), leading with positive instructions is usually more effective.
Example:
Instead of: "Generate a blog post about video game consoles. Do not list video game names."
Try: "Generate a 1-paragraph blog post about the top 5 video game consoles. Only discuss the console name, the company who made it, the release year, and total sales."
Give these techniques a try! Experimenting with how you frame your requests is key to unlocking more of the power and potential within these large language models. Happy prompting!