I’ve been trying to have more natural, human-like conversations with different AIs, but my prompts either feel too stiff or I get generic answers that don’t really help. What’s the best way to “chat up” an AI so it responds more personally and in-depth, without sounding robotic or confusing it? Any tips, examples, or prompt structures that actually work would really help me out.
Short answer. Treat the AI like a mix of a search engine, a junior coworker, and a strict genie.
Some practical stuff that usually gets better responses:
- Say what you want, not only the topic
Bad:
“Explain AI alignment.”
Better:
“Explain AI alignment to me like I am 15, in 5 bullet points, max 2 sentences each, no math.”
You define level, format, and limits. That steers it away from generic essay sludge.
- Give quick context about you
One sentence is enough.
“I know basic Python, but I struggle with data structures.”
“I am a beginner in fitness, low budget, bad knees.”
The model uses that to set difficulty and pick examples.
- Show your goal, not only your question
Bad:
“How do I use vector databases?”
Better:
“I want to build a small app that searches my notes. I use Python. I want fast search on short text notes. What tech stack and steps would you suggest?”
Goal gives it a target. Answers get more concrete.
- Ask for structure
Tell it how to format.
“Reply in this format: - Short answer
- Step by step plan
- Example
- Common mistakes”
Or
“Give me a checklist, no paragraphs, 10 items max.”
That cuts down on waffle.
- Talk like you are giving instructions to a person at work
Instead of being vague and “natural”, be specific but casual.
Example prompt style that works well:
“I am trying to understand transformers. I know basic neural nets.
Do this:
- Compare transformers to RNNs in simple terms.
- Give one concrete example with numbers.
- Then give me 3 resources to read next.
Use short paragraphs.”
This still feels like a chat, but it has clear instructions.
- Use “iterate with me” prompts
If the answer feels generic, tell it what you liked and what sucked.
“Your answer was too high level. I want concrete steps, not theory. I liked point 3, expand that with an example.”
or
“Give me the same answer, but shorter, and focus only on practical steps I can do today.”
You train the response style over a few messages.
- Ask for questions back
This helps kill generic replies.
“Before you answer, ask me 3 questions to clarify my situation.”
or
“First, ask what you need to know about my skill level. Then propose a plan.”
If it stops asking, remind it.
- Show a sample of what you want
If you have a response you liked from somewhere, paste it.
“I like responses like this:
[example]
Give me answers in a similar style: short, concrete, no fluff, with 1 simple example each time.”
The model will pattern match.
- Use constraints
Constraints stop rambling.
“Max 150 words.”
“No stories, only steps.”
“Use plain language, no jargon.”
“Number the steps 1 to 5.”
If it ignores a constraint, repeat it:
“Shorter. Under 100 words.”
“Less theory, more ‘do this then this’.”
- For “natural chat”, still give small objectives
Instead of “Let’s talk about philosophy”, try:
“I want to explore free will vs determinism. Act like a thoughtful debate partner.
Your job: - Ask me one question at a time.
- Challenge my answers respectfully.
- Bring in specific arguments from known philosophers.”
Feels like a human chat, still uses clear roles.
- Call out bad behavior, like you would with a human
“Too generic. You repeated yourself.”
“You ignored my request for examples. Try again, with 2 examples.”
“This sounds like a blog post. I want notes for myself.”
Feedback in the same thread usually improves the next answer.
- Use a “default style” prompt you reuse
Save a base prompt that you paste at the top sometimes.
Example:
“When you answer me, follow these rules:
- Prefer short, clear sentences.
- Prioritize practical advice over theory.
- Ask a clarifying question if my prompt is vague.
- Use examples wherever possible.
- If you do not know, say so and give me options to check.”
Then ask your normal question after that.
- Avoid super vague vibes
Prompts like:
“Talk to me like a friend.”
“Be more human.”
These usually give you fluff. Pair that with specifics instead:
“Talk to me like a friend who is good at data science and a bit blunt.
Help me figure out if my project idea makes sense. Ask me 5 clarifying questions first.”
- Use follow ups, not giant monologues
It helps to break problems into steps.
First:
“I want to learn web dev. Ask me questions to figure out my level and time budget.”
Then:
“Ok, now build me a 4 week learning plan.”
Then:
“Now, turn week 1 into a daily checklist.”
This beats a single huge “teach me everything” prompt.
- If you want “human-like”, tell it the tone
“Use casual language, but stay precise.”
“Talk to me like a senior engineer mentoring a junior.”
“Talk to me like a teacher who respects adult students, not like I am a kid.”
You do not need flowery stuff. Direct tone instructions work fine.
Quick template you can tweak:
“I am [your background].
My goal today is [goal].
My constraints are [time, skill, tools].
Your role is [teacher / coach / debate partner / editor].
Please respond with:
- Short answer
- Step by step plan
- One example
- One question for me to answer next.”
Paste that, change the details, then add your specific question.
Two things to add on top of what @sternenwanderer already laid out:
- Stop trying to be “natural,” start trying to be transparent
“Natural” human chat is actually pretty ambiguous:
- “So I’m kinda stuck with my startup idea, thoughts?”
Humans infer tone, history, mood. Models mostly don’t. You think you’re being casual; the AI just sees a vague request and falls back to generic patterns.
Try this instead, which feels like chat but is clearer:
“I’m stuck on my startup idea and I’m not sure if it’s actually useful.
I’ll paste the idea. Your job:
- First, tell me in 3 sentences what you think the core problem is.
- Then list 5 blunt reasons it might fail.
- Then 3 ways to test it in 1 week, super low budget.”
That’s still conversational, but you’re explicitly telling it: role, tasks, format. “Natural” for AI = explicit, not vague.
- Show your uncertainty instead of hiding it
A lot of people try to sound smarter or more “together” when they ask, and that backfires. The model then assumes higher expertise and gives higher level stuff.
Compare:
- “Explain the mathematics of diffusion models and their training objectives.”
vs - “I only kinda get what a neural net is. I’ve watched 2–3 YouTube videos and that’s it. I’m mostly curious, not doing research.
I saw ‘diffusion models’ mentioned.
Can you:
- Explain what they do in plain language
- Use at most one super simple analogy
- Skip detailed math, just say what’s important conceptually”
The second is way more “human” in practice, even if it looks more like instructions. You’re giving the kind of context you’d give a friend: level, motivation, and what you don’t want.
Some places where I slightly disagree with the “treat it like a junior coworker” thing:
- Sometimes you want it as a mirror, not a coworker
If you’re stuck thinking, you don’t always want steps. You want reflection. Try:
“I’m going to rant for a bit about my job. Your role:
- Don’t fix it yet.
- After I’m done, summarize what you think I’m actually worried about in 5 bullets.
- Then ask me 3 questions that would help you understand the situation better.”
That gives you a more human-feeling back-and-forth: you talk, it reflects, then asks. Less “task bot,” more “thinking partner.”
- You can sometimes intentionally underspecify, but control the scope
Vague question + tiny scope works better than vague question + huge scope.
Instead of:
“Teach me philosophy.”
Try:
“I’m low on focus, just want a short, fun chat.
Pick one classic philosophy idea that most people misunderstand.
- Explain it to me in under 200 words
- Then ask me 1 question about whether I agree
- Then push back on my answer like a slightly annoyed philosophy teacher”
That keeps it small, so “generic mode” has less room to appear.
- Let the AI “see” your taste and personality
People almost never do this, and it’s one of the fastest ways to make it feel less generic.
Examples:
- Paste something you hate:
“Here’s the kind of answer I can’t stand:
[paste generic article-y answer]
Stuff I hate about this:
- Too polished
- Reads like a blog post
- Zero personality
When you answer me, avoid this style. Write more like messy thinking notes to yourself, but still clear.”
- Or paste something you like and explain why:
“I like answers that:
- Don’t try to sound smart
- Use concrete examples from daily life
- Admit uncertainty
Try to match that vibe in your answer about X.”
You don’t need to repeat this every time; just refresh it occasionally in a long thread.
- Let it help you shape the question
Instead of trying to craft the perfect prompt alone, outsource that part too.
For example:
“I want help with my sleep problems, but I’m not even sure how to ask about it.
Your first job: ask me 5–7 specific questions that will help you give a actually useful answer, not generic tips.
After I reply, THEN you give advice, in 10 bullets max.”
This fixes a common issue: users ask fuzzy questions, get fuzzy answers, then blame themselves for “bad prompting.” Let the model participate in prompt design.
- Be emotionally honest, not “prompt smart”
You don’t need to sound like a spec writer all the time. You can write:
“Honestly I’m tired and don’t want to think that hard.
I just need:
- 3 simple options
- With 1 reason for each
Keep the tone low-key, not ‘motivational poster’ style.”
Or:
“I’m anxious about this topic, so please:
- Don’t be alarmist
- Don’t overly sugarcoat either
- If something is uncertain, say ‘we’re not sure’ instead of pretending.”
Models can pick up and respect that kind of emotional boundary surprisingly well. It also kills that generic hyper-optimistic tone a lot of answers have.
- Use “ongoing project” mode across days
If you talk to it like every question is a one-off, it’ll answer like that. Treat it like an ongoing doc:
First day:
“We’re starting a ‘project’ to help me learn basic statistics.
Your role: tutor.
Today’s goal: figure out exactly what I already know and what I don’t.
Ask me up to 15 short questions, then propose a 2-week plan.”
Next day:
“Same project, same role.
Today I want to cover only: averages, variance, standard deviation.
Give me:
- One-sentence definition each
- One real-life example each
- 3 practice questions with answers hidden until I ask.”
Longer-term “shared context” + clear day-by-day goals feels a lot more like a real human mentor than one giant mega-prompt.
- Don’t be afraid to say “this is boring, change the style”
A lot of people just silently accept an answer they dislike. Call it out like you would with an actual person:
- “This reads like a textbook. Try again like you’re explaining it at a bar to a friend who’s smart but tired.”
- “Too much theory. Keep only the advice that leads to something I can do in under 1 hour.”
- “You’re hedging too much. For this question I prefer a strong opinion, even if you have to simplify.”
That kind of direct feedback in the same thread usually shifts the vibe a lot.
If you want a minimal template that keeps things “chatty” but effective, something like:
“Context: [what’s going on / my level / my mood]
My goal: [what I want out of this conversation, not just the topic]
Your role: [coach / devil’s advocate / explainer / therapist-ish listener, etc.]
Style: [short / blunt / reflective / casual / etc.]
Constraints: [word limit, examples, questions back]
First task: [what you should do in this first reply only]”
And yeah, you can be casual with the wording. Typos are fine, the model does not care. Over-optimizing the exact phrasing of every word is usually a waste of energy compared to just being clearer about your goal and your taste.