Your AI Tool is Only as Good as Your Prompt
Photo by Volodymyr Dobrovolskyy on Unsplash
Gone are the days when AI was a futuristic concept; it's now a practical, everyday tool. From creating and debugging blocks of code and writing documents, to editing images for social media, everyone is turning to AI to get things done faster and boost their creativity. However, many users get frustrated when the AI's answers are vague, incorrect, or not what they had in mind. The problem isn't the AI itself – it's the instructions we give it.
Getting Used to AI Tools
For a software engineer with experience in AI, it's ironic that it took a colleague to convince me to actually use these tools. My initial resistance wasn't just a fear of my skills getting rusty; it was fueled by genuinely frustrating experiences. I dealt with unhelpful results, bizarre AI hallucinations, and on one memorable occasion, had my progress completely wiped out. It took time, but I eventually realized the issue wasn't the AI. The problem was me – I was passively expecting the tool to read my mind instead of actively guiding it.
Driven by this realization, I dug into research papers and articles on how AI models actually think.
The AI Blind Spot
Humans and AI "think" in fundamentally different ways. We possess genuine consciousness and comprehension, allowing us to understand concepts abstractly. In contrast, an AI achieves what looks like "thinking" through sophisticated pattern matching. It doesn't truly understand; it simply calculates the most probable sequence of words to follow based on the vast patterns in its training data.
Another concept that I learned is the idea of implicit intent – an unspoken intention that we, as humans, easily understand through self-awareness and emotional maturity. We are adept at implying things and still being understood while AI can't interpret vague questions, fill in contextual gaps, or process implicit requests. Without explicit instructions, an AI is unable to infer a user's underlying goals or assumptions.
Context is King
While simple questions may only require simple prompts, software engineering demands a far more sophisticated approach. I learned this firsthand while working on a legacy codebase, tasked with fixing a bug in a decade-old project.
Despite having access to the entire codebase, the AI tool struggled because its training data is modern. It couldn't identify the root cause and kept suggesting functions that weren't available in the old framework, causing runtime errors.
My solution was to provide the missing context: I used a second AI agent to research the specific version of the legacy framework, summarize the relevant functions, and feed that information to the primary AI coding tool. With added knowledge, the tool was finally able to help me identify and fix the bug.
The Art of an Ask
To get more things done faster and more efficiently, I follow these four key principles when using AI tools:
Be Clear and Concise. Keep instructions brief and to the point. For complex tasks, break them down into smaller, more manageable steps.
Provide Rich Context. Give the AI all the relevant background information, constraints, examples, and desired outcomes so it understands the full picture.
Iterate and Refine. Don't settle for the first response. Ask the AI for improvements, optimizations, or alternative approaches to find the best possible solution.
Act as the Final Authority. Always verify AI's output. You are the source of truth, so test the response for factuality, accuracy, and potential edge cases.
If you take away just one thing from all I wrote, let it be this: AI cannot read your mind. It relies completely on the clarity of your instructions. The more effort you put into asking, the more value you'll get back.