The Conversation Isn’t Real – Lon Riesberg

Most people prompt like they’re having a conversation. Ask ChatGPT a question, it responds, you respond back, and so on. It’s a natural flow for people and it works, but understanding why chatting with a chatbot is NOT a conversation will supercharge how you use AI.

Here’s what’s actually happening. Every time you hit send, the entire chat history gets bundled up and handed to a fresh instance of the model that has never seen you before and will never see you again. It reads the entire transcript, generates a response, and then vanishes. There’s no session, no memory, no “the model I’ve been talking to.” What feels like a conversation is really a document that gets re-read from scratch on every turn.

That might sound like a limitation, but it’s actually a superpower. You can do things to a document that you can’t do to a relationship. You can hand a document to a different model, fork it, delete bad ideas, and then watch the next response reason as if those edits never existed.

In this post, I’ll walk through three tiers of working with LLMs, and show you specific ways to get more from your prompts. Tier 1 shows how to get more out of a single chat. Tier 2 is where you escape the limits of being in a “conversation.” Tier 3 is where you notice that what you’ve been doing by hand in Tier 2 is actually a pipeline, and pipelines can be built.

You don’t need to be an engineer for any of this. Tiers 1 and 2 are pure copy-paste. Tier 3 is a way of thinking that happens to point toward code.

Tier 1: Talk to One Model Better

Out of the box, a chatbot has two default behaviors that quietly cap the quality of everything it gives you: it guesses your context instead of asking for it, and it defers to you instead of pushing back. Every technique in this tier fixes one of those two things.

1. Get interviewed first

The number one failure mode with these tools is that the model doesn’t know your context, so it guesses. The fix is to stop it from guessing:

Please draft a job description for a senior analyst on my team. Before writing anything, ask me the 5 questions whose answers would most change the draft.<br>The model asks about team size, stakeholder mix, whether your culture is SQL-first or Python-first. They’re things you knew but forgot you knew.

Sometimes the interview does something better than improve the deliverable: it kills it. Ask to be interviewed about a dashboard you’re planning, and don’t be surprised if the questions reveal that it shouldn’t even exist.

2. Force a choice

Ask “should we use Postgres or BigQuery?” and you’ll get a response that essentially ends with, “it depends.” Instead, do something like this:

Postgres or BigQuery for a 50GB analytics workload, 3-person team. Pick one. You get one sentence of justification, then stop.<br>You can always ask for the caveats afterward. The point is that you now know where the model actually lands, instead of watching it straddle the fence.

Forced ranking is even better than forced choice. “Rank these five KPI candidates from most to least useful for a subscription business. No ties allowed.” Ties are where models hide.

3. Run a pre-mortem

“Any concerns about this plan?” gets you polite, generic risks. This gets you something else:

We're launching a self-serve analytics tool for non-technical PMs. Imagine it's 8 months later and adoption is near zero. Write the internal post-mortem, including the warning signs everyone ignored.<br>The framing does the work. Phrases like “internal post-mortem” and “warning signs everyone ignored” produce specific organizational failure modes instead of a risk checklist.

It works on artifacts too, not just plans. Take a marketing email you’re about to send: “imagine it got a 0% response rate and tell me why” is much sharper than “how can I improve this email?”, which will mostly get you tone tweaks.

4. Roleplay your audience

You're a data analyst with 4 years of experience reading this tutorial. React in real time: quote the exact line where you first got bored, the line where you got skeptical, and the line where you'd close the tab.<br>Demanding quoted lines is the trick. Without it, you get “the middle section drags a bit,” which is useless. With it, you get a line number.

A variant I like for charts: “You’re a CFO seeing this in a board deck with 10 seconds of attention. What do you take away? Now, what did the author intend you to take away?” The gap between those two answers is the entire critique.

5. Ask what you should have asked

Say you just got advice about a database migration. Before moving on, add one more message:

What should I have asked you that I didn't?<br>This often surfaces the deal-breaker. “You never asked about your downtime tolerance.” The model wouldn’t volunteer that, because you didn’t seem to want it.

This one’s even better: “What’s the question that, if I answered it, would most change your recommendation?” That one forces the model to reveal...