10 Questions no other AI tool can answer #OnlyInPieces

I had this humbling moment on a support call this week. A user asked what seemed like a perfectly reasonable question: "Remind me again on what the team ended up deciding on how to handle merge conflicts. I know we were talking about this a couple weeks back."

If you've ever used ChatGPT, you know exactly what would happen next – ChatGPT would have no idea. You are stuck digging through old chats, reminding it of the conversation, and scrolling to find it. 

Pieces?

Oh, it knows! It saw everything you did in your IDE, on Slack, or Gchat, in Github, your email threads, it has the full picture, And the best part is that all of this context, all the details from however many weeks ago, is stored inside of the piece's long-term memory and ready to be resurfaced as context.

That’s exactly why we’re building artificial memory across your OS and desktop so your favorite LLM becomes truly helpful, personalized, and contextually aware.

The result? Not just one-off answers, but relevant insights that resurface the who, what, when, where, why, and how so you see the bigger picture and the details that matter most.

Three real stories that show the difference

You won't believe the types of questions that you can ask pieces. So below, we're showing you three real-world stories from our users and team members on the types of questions that they simply can't ask any other AI platform out there. 

By the end of this blog, we hope you'll be as fired up about artificial memory as we are!

Rather than boring you with feature lists, let me walk you through three actual scenarios from our team. Same prompt, two different responses, totally different outcomes.

Story 1: When engineering coordination actually works

Judson, who leads our technical documentation, was trying to remember a decision from the day before. He typed: "Based on yesterday's events, what strategy did Nolan and I decide to minimize merge conflicts while still trimming fat?"

ChatGPT's response: 

A generic, if helpful, cleanup strategy. The kind of safe advice you'd get from any coding blog.

Pieces' response: The actual agreement they made, "Cease Fire & Sequential Merging" with exact timestamps, quoted conversations, and a direct link to the PR where they started implementing it.

Judson told me later that was the first time he'd gotten a recap that didn't feel like rebuilding his memory from scratch. He could just... continue working.

Story 2: Research that doesn't disappear into the void

Our Senior Dev Advocate, Ali, runs dozens of webinars and talks. He's constantly reading, constantly researching. Last week he asked: "What were those GenAI articles I was reading last week?"

ChatGPT: The familiar "I don't have access to your browsing history" response.

Pieces: A complete timeline. Directly recalls your personal activity from last week , including time stamps even music you listened to. Acts as a personal memory layer for your research.

Ali didn't realize until that moment how much mental energy he was burning on "where was that link again?" Suddenly he had a digest he could actually act on. When you're at a startup juggling 5-10 projects simultaneously, that kind of instant recall becomes a superpower.

Story 3: Debugging with actual evidence

This one still makes me smile. Our engineer, Bishoy, was dealing with user feedback about our CLI tool, and coincidentally, our Sentry logs were lighting up with errors the same day.

He threw this brutal prompt at both systems: "Summarize Nolan's feedback, link the related GitHub issue, pull today's Sentry errors, check if they're connected, and show where to fix it."

ChatGPT: A reasonable "here's how you might investigate this" checklist. Good process guidance, but you'd still need to do all the detective work yourself.

Pieces: Went full Sherlock Holmes. It connected Nolan's original feedback message to GitHub issue #400, pulled today's specific Sentry errors (WinError 10054 and 10055), identified the problematic files like chat_panel.py, and even suggested actual code patches to fix the WebSocket reconnection issues.

That moment felt genuinely spooky, not because it hallucinated some connection, but because it remembered and connected real artifacts from our actual work.

Why this feels so different

GPT is still the reasoning engine doing the heavy thinking. But Pieces adds the memory and grounding layer that makes that reasoning actually useful for real work.

It's the difference between:

• Brilliant reasoning from nothing vs. Brilliant reasoning from everything you've actually done

• Generic best practices vs. Your team's specific decisions and context

• Starting over every conversation vs. Building on yesterday's progress

The workflow difference is everything. Instead of being a smart conversation partner, it becomes a smart teammate who was actually there for all your previous work.

10 Questions you can actually ask (copy these)

Here's the best part – some real prompts our users are already running. Feel free to copy-paste these and adapt them:

1. "What did [teammate name] and I decide yesterday about [specific topic]? Quote the exact lines and link to the PR."
   
   

2. "Summarize everything I read about [topic] last week across LinkedIn, GitHub, and any articles I opened. Include timestamps and links."
   
   

3. "Show me today's errors that match [specific feature/component], map them to the actual files, and propose patches."
   
   

4. "What were the main blockers discussed in our last three standups? Connect them to any related GitHub issues."
   
   

5. "Find that conversation where we debugged the [specific bug]. What was the root cause and how did we fix it?"
   
   

6. "What documentation did I bookmark about [technology] this month? Give me the key points from each."
   
   

7. "Connect this error message to any previous discussions. Have we seen this before and how did we solve it?"
   
   

8. "What feedback have we gotten about [feature] across Slack, issues, and support tickets? Summarize the patterns."
   
   

9. "Show me all the times [teammate] mentioned [specific concern] and whether we've addressed it yet."
   
   

10. "Based on our recent conversations about [project], what are the next concrete steps with links to relevant docs/issues?"
    
    

Create your first memories

Each of these questions works because Pieces isn't just remembering individual conversations, it's connecting dots across your entire workflow. That Slack message connects to the GitHub issue, the error log connects to yesterday's debugging session or the research article connects to the feature decision you made last week.

It's not replacing your thinking. It's just making sure your thinking builds on what you've already figured out, instead of starting from zero every single time.

Ready to try it? These prompts work best when Pieces has been running in the background, learning your workflow. The more context it has, the more connections it can make.