Ai Prompt Engineering for Government

Andre

Hey, everyone! Welcome back to Engaging Thought. I’m Andre, and I have Eric here with me. Today, we’re diving into the sometimes-buzzy, sometimes-baffling world of prompt engineering. Eric, have you had one of those moments where you ask ChatGPT something and it just gives you the most unhelpful answer possible, like it’s your quirky uncle instead of a cutting-edge AI?

Eric Marquette

Oh, all the time, Andre. And sometimes it’s hilarious, but other times it’s like—hey, I really just needed a straight answer, and instead I’m in an endless Shakespeare monologue. And you’d think, with all the AI hype we hear about these days, the secret sauce would be in the tech, but a lot of the time, it comes down to how you ask the question—the prompt. That’s where prompt engineering comes in.

Andre

Exactly. So, let’s ground this: prompt engineering is, simply put, the art and science of crafting inputs—prompts—for AI tools so you get better, more relevant outputs. Think of it like giving directions to someone who’s never been to your city before. If you say, “Go downtown,” it’s vague—they’d probably wander around lost. But if you say, “Take Main Street to 2nd Avenue, park by the coffee shop with the blue door, then walk north,” suddenly, things click. Well, prompt engineering is a bit like that, but for AI.

Eric Marquette

Right. And this isn’t just about making ChatGPT write poems for fun, though that’s always entertaining. Prompt engineering can actually control AI intent, target responses, help avoid bias, and even optimize user experience. And—maybe most importantly—it reduces those “AI hallucinations,” where the system just confidently makes stuff up. I mean, that’s a whole rabbit hole right there.

Andre

And those hallucinations, they’re tricky because sometimes the answer just feels right, but it’s completely off the mark! So, the idea is, with better prompts, you get more accurate, relevant results, and—hopefully—spend less time double-checking everything the AI spits out.

Eric Marquette

Exactly. And if we look at practical examples—a local government, for instance, might use prompt engineering to draft clear communication for residents, research best practices, or even summarize dense regulatory documents. You start to see how the stakes get higher than, say, writing a silly story about your dog.

Andre

So let’s zoom in on the actual techniques here, because there’s more than one way to do this. The first is what’s called zero-shot prompting. Eric, you like analogies—got one for this?

Eric Marquette

Haha, let’s see—zero-shot prompting is kinda like tossing someone onto a basketball court and saying, “Score a basket,” but giving them no hints about the rules, nothing about dribbling, nothing about fouls. You just expect them to figure it out from scratch. In AI speak, it’s about asking the system to do something without showing any examples first.

Andre

Exactly, and sometimes that actually works—but not always! So, to help, there’s a method called few-shot prompting, which, unsurprisingly, gives the AI a few examples to follow before you ask for the real answer. It’s like setting a standard before letting the model try.

Eric Marquette

And then there’s my favorite: chain-of-thought prompting, or CoT if you’re deep in the AI weeds. That means you ask the model to break down a complex problem step by step—much like showing your work in algebra. It’s especially good when you want AI to reason through something, not just spit out a canned answer.

Andre

Yeah, and it definitely brings a more human touch to AI reasoning. Then you have interactive prompts—where you iteratively refine what you’re asking. Basically, you try a prompt, look at what comes out, tweak your wording, or add directions until the AI finally nails what you need. Not an exact science, but super effective in practice.

Eric Marquette

We talk theory, but let’s get practical. There are some serious uses for prompt engineering, like in natural language processing. Think summarizing news articles, translating languages, or even condensing huge policy docs into bite-size pieces people can actually read. You just have to give the AI a nudge in the right direction.

Andre

Exactly. And for chatbots—whether in city government, health, or animal control—the prompts you design are critical. For example, say you want a virtual assistant to help a resident schedule a recycling pick-up. The prompt chains should pull in the current day’s schedule, address-specific rules, and translate it if needed. If you botch the prompt, suddenly people get their recycling picked up in March instead of May—frustration city.

Eric Marquette

Yup. Another one is content generation. This is where you tell the system not just “write this,” but “write it as a 200-word summary, in plain English, at an 8th grade reading level, and make it sound like a friendly neighbor.” Prompting with that specificity is a total game changer. And it works across government communications, medical explanations, and more.

Andre

Don’t forget question-answering systems. These are everywhere now. The AI needs to give succinct—but complete—responses even if it doesn’t know your full context. Prompt engineers have to whittle down big, messy questions into super clear bits the model can handle. A little like teaching someone to answer “Who won the World Series in 1999?” instead of, “What are sports?” if that makes sense.

Eric Marquette

That’s a good one, Andre. And let’s mention recommendation systems, too. Even things like city services, product purchasing, or medical resource tools rely on prompt engineering to tailor responses for each user. The more on-point your prompt, the more relevant and helpful the result.

Andre

So, how do you actually get better at prompt engineering? There’s a whole checklist, honestly. First, defining your desired outcome—what do you really want? You can’t just hope the AI reads your mind, right?

Eric Marquette

Exactly. Be intentional. Next, decide the right format. For complicated outputs, you need to spell out exactly how you want the answer packaged. Sometimes that means tables, summaries, a certain tone, or even bullet points.

Andre

Adding to that—be specific and explicit. The AI doesn’t know what to exclude unless you tell it. Say you only want a list of inventors from the 1800s—specify the time period, the number of inventors, and even ask for a table if you want. Details matter, or else you’re playing “prompt roulette.”

Eric Marquette

You also want to balance complexity and simplicity. Too vague, and the AI flounders. Too much jargon or too many instructions, and you get, well, something that doesn’t make much sense at all. So try to be concise but thorough—easier said than done, but it’s worth practicing.

Andre

And don’t be afraid to iterate or experiment. Prompt engineering isn’t a “set it and forget it” game. Try, tweak, and repeat. You won’t get it perfect the first time. Even pros keep refining. And, as we covered in a previous episode, feedback and iteration are just as vital in prompt engineering as anywhere else in problem solving.

Eric Marquette

I like that you brought up iteration. Also—set output length when possible. If you want three bullet points, say so. If you need a full page, request that. The model won’t always get it spot on, but at least you’ll get closer to what you envisioned. Oh, and avoid ambiguous language—don’t say, “Give me a detailed summary.” That’s kind of, uh, conflicting. Be clear about the level of detail you’re after.

Andre

Use context and examples, too. If you want a specific style, show it. Need the answer for a specific audience? Say so. And watch your punctuation—lack of commas or periods can totally throw off more complex prompts. Seriously, sometimes AI is like a computer that never learned how to read run-on sentences!

Eric Marquette

Alright, so what can trip you up? Well, the biggest one I see is treating prompts as one-size-fits-all. Each AI tool has different quirks. A prompt that works for ChatGPT could flop on Gemini or Perplexity. You have to tailor it, just like you’d change your tone talking with your boss versus your best friend.

Andre

And don’t just stick with default settings. For instance, in some tools you can tweak something called “temperature”—that’s how creative or random the output will be. Play around! Don’t look for quick, one-word answers either. You’ll get more depth if you ask open-ended questions, like, “Describe the factors that led to…” instead of, “Did it happen?”

Eric Marquette

Another classic trap—assuming AI is always right. We touched on hallucinations earlier—AI can make confident, completely false statements. It’s important to always fact-check, especially when making decisions off those results. Oh, and don’t be afraid to revise, switch up prompt structure, or break up complex requests into smaller tasks.

Andre

And you know, sometimes just switching the order of instructions in a prompt makes all the difference. If it feels like AI just isn’t getting what you want, try shuffling instructions, or even start over with a fresh approach. Don’t get stuck blaming the model before experimenting with your prompt.

Eric Marquette

Andre, seeing as you’ve worked with organizations on this, can you share some examples of prompt engineering in public service settings?

Andre

Absolutely. Let’s say you’re in Public Works and need to notify residents about water line shut-offs. Using ChatGPT, you can create simple, readable messages in multiple languages. If you want to check new EPA guidelines for water treatment, you prompt Perplexity to pull up and summarize the latest regulations, citing sources. Need to summarize a whole week’s worth of service calls for reporting? Gemini can handle those dashboards and reports right inside Google Sheets—if you prompt it right.

Eric Marquette

And Grok’s really useful when you need to scan real-time social media or news chatter—like tracking complaints about road construction delays, or even monitoring sentiment around a new city policy. If you build well-crafted prompts for each tool, the potential for efficiency, equity, and clarity in the public sector is kind of huge.

Andre

Completely agree. Each AI tool has its lane—don’t ask a hammer to do a screwdriver’s job. Start with your problem or goal, then match it to the tool, and only then craft the prompt. That’s the simplest but most effective advice I can give.

Eric Marquette

Alright, so to wrap up: prompt engineering isn’t about being a tech mastermind—it’s about experimenting, communicating clearly, and learning to see AI as a helpful partner, not a mind reader. Whether you’re in local government, tech, or just tinkering at home, good prompts mean better results and less frustration.

Andre

Exactly. And, you know, as we’ve talked about in earlier episodes—like on responsible AI and building public trust—none of this works unless people are willing to try, learn, and adjust. Mess up, fix it, and keep moving. That’s it. So thanks everyone for joining us today, and Eric, always a pleasure. Ready for the next round soon?

Eric Marquette

Of course, Andre! Thanks for listening, folks. If you have prompts you want us to break down, or public sector questions you’re wrestling with, send them our way. Watch for the next Engaging Thought—see y’all soon.

Andre

Take care and keep those prompts sharp!