When building LangChain agents, understanding how your LLM "reasons" isn’t just a technical curiosity, it’s critical for debugging. They operate as probabilistic engines trained on vast datasets, generating outputs that mimic reasoning, planning, and even self awareness. Whether they’re glorified autocomplete systems or emergent cognitive frameworks, they’re shaping how we build, debug, and deploy intelligent workflows. To truly understand what these models can and can’t do, I went straight to the source. I prompted ChatGPT, Microsoft Copilot, and Google Gemini to explain their own architecture, reasoning process, and limitations. The best way to learn how they think is to ask them to think about themselves. Here’s what they said.
Questions Asked
- How do you work?
- Can you explain more technically?
- What’s the actual math behind it?
- How do you create your own output with reason if all you are doing is token prediction?
- Can you explain it step by step like I’m 5?
- What are your limitations today?
Response Summary
| Question | ChatGPT | Copilot | Gemini |
|---|---|---|---|
| 1. How do you work? | Describes token prediction, training on large datasets, and emergent reasoning. | Explains transformer architecture and modular orchestration. | Covers transformer basics and training data. |
| 2. Can you explain more technically? | Introduces embeddings, attention, and sampling with equations. | Details inference pipeline, memory, and tool integration. | Describes self-attention and model layers. |
| 3. What’s the actual math behind it? | Shares softmax and sampling logic. | Explains conditional probability and token scoring. | Mentions attention weights and vector math. |
| 4. How do you create your own output with reason if all you are doing is token prediction? | Frames reasoning as emergent behavior from token selection. | Simulates reasoning and planning via intermediate steps and tool calls. | Says reasoning emerges from scale and training. |
| 5. Can you explain it step by step like I’m 5? | Uses analogies like smart autocomplete and storytelling. | Describes step-by-step planning like a checklist. | Uses library and storytelling metaphors. |
| 6. What are your limitations today? | Hallucinations, context limits, bias, no memory. | No consciousness, no intent, sandboxed tools. | Hallucinations, bias, static knowledge, no real reasoning. |
Verdict: Who Explained It Best?
| Model | Clarity | Depth | Conciseness | Dev Utility |
|---|---|---|---|---|
| ChatGPT | Y | Y | Y | Y |
| Copilot | Y | Y | Y | Y |
| Gemini | Y | Maybe | Maybe | Maybe |
Legend - Y (It's able to explain in detail); Maybe (Somewhat explained it)
Takeaway for Agent Builders
If you're designing agents that need to simulate reasoning, learn and make decisions. Copilot’s modular thinking and planning metaphors are especially useful. ChatGPT is great for understanding the math and mechanics. Gemini is fine but less actionable.
Don't forget to try it on your own and let's discuss in the comments.
Versions used -
Gemini 2.5 Flash
Copilot Quick Response Mode
ChatGPT GPT-5
Top comments (1)
AI: 90% token math, 10% vibes. Debug accordingly.