The Power of Context: An Accidental Experiment in Understanding

The Discovery

During a conversation about AI chat persistence, I stumbled upon a fascinating opportunity: a live demonstration of how different prompting approaches yield dramatically different results. The setup was elegantly simple – start multiple fresh conversations, ask the same question about persona perception, but vary the context leading up to it. I broke this down into 3 levels of iteration.

Level 1: No Context

My first attempt was straightforward: a direct question about persona perception with no context. This literally involved me starting a new chat with Claude and pasting the following prompt without any additional information:

As I expected, I didn't get anywhere. It was like asking someone to describe me at the moment we met - surface-level and generic.

Level 2: Moderate Context

For my second attempt, I recreated the entire conversation exchange that I had about integrating AI into my internet browser. This chat was identical to the one I had only a few minutes earlier, but in a new window. I stopped once I asked for the bot's perception.

Now, I've been able to obtain a workable response from Claude, which clearly shows an understanding of my technical knowledge and analytical thinking patterns. It does a pretty good job of summarizing me too!

Level 3: Extensive Context

At the conclusion of my initial conversation (not the two recreations), I asked the bot the same question one last time. This time, however, it had the entirety of our prior conversation along with all the iterative feedback I provided along the way.

The response evolved dramatically, providing substantial critical thinking and perception. Claude now yielded insights that demonstrated deep understanding of my approach and methodology (I'll admit, I found this pretty cool).

Context is Essential

This experiment further showcased the importance of including three key principles:

1. Context Depth Matters - Basic prompts can only provide basic understanding. The more information I provided coupled with a practical structure shaped improved outcomes.
2. Sequence is Critical - The journey through each prompt is as important as the last. Providing a solid foundation and building upon it reshapes future responses to improve understanding.
3. Validation Through Testing - Systematically testing my approaches clearly demonstrated improved effectiveness. I can objectively measure the quality of my outputs in relationship to each other.

Ultimately, the purpose of this conversation (or any other one that I've had with AI) isn't just about getting answers. It is about changing my strategies and tactics to create a repeatable system. This is what enables me to consistently have effective conversations across a wide variety of chats about different topics.

The Key Insight

The most valuable revelation should not be that context matters in prompting. Yes, it is extremely important, but it's that systematic context building is what transforms AI interaction in measurable, replicable ways. As AI tools become increasingly more integrated into our daily lives, prompting effectively becomes increasingly crucial.

I'm able to repeatedly achieve my goals with these tools because I follow a simple framework, one that I discovered after hundreds of hours of using several different Generative AI tools. I call it PORC (think "pork" - like a pig)!

Purpose - explain what you are trying to achieve. Aim to be as simple and as specific as you can.

Output - tell the machine what kind of format the finished product needs to be. Is it an image, a spreadsheet, a web page, an essay, something else?

Requirements - expand on the parameters that you will need to follow to achieve your finished product. Use examples like "a finished product will include the following."

Context - provide as much background about the topic as possible. The more information you provide, the better your output will be.

If you look through the conversations I had about these topics, you'll notice that I incorporate PORC into every single prompt. This consistency is essential - it is what ensures that I achieve my desired outcome.

What's Next

Practice - it is that simple. Take the time to continually apply these ideas into your own work. You don't need to start with something big - try something otherwise forgettable, like where to go to lunch for today or asking about current news trends. As long as you are getting more comfortable with these tools, consider it a win.

If you still aren't sure about where to begin, be sure to stay tuned! I'm close to releasing a new resource that will offer a variety of practical (and tactical) pages that focus on improving your prompt engineering skills.

Author's Note

This article emerged from a real experiment in AI interaction that accidentally demonstrated these prompting principles. The best frameworks often come from practical experience rather than theoretical design.