Q × K

🔴 Core

Area03 · Process Context Efficiently

TopicQKV

Topic priority🔴 MUST

Subtopic4 of 7

🖼️ Master QKV Architecture Infographic

How Query Meets Key ($Q \cdot K^T$) to Calculate Attention Scores

Observe step 4 below: The Query vector for 'capital' is compared against all token Keys via dot product, scoring highest for 'India' ($0.36$) and itself ($0.41$).

Understanding Query, Key, and Value (Q, K, V) Attention Infographic — Click infographic to view full resolution in a new tab.

Study this using 8 phases

🧠 01 Understand

What is Q × K? Why does it exist? Where does it appear in QKV?

👁️ 02 Visualize

Draw or describe a visual model of Q × K. What does it look like conceptually?

✏️ 03 Calculate

Work through a concrete manual example of Q × K with real numbers or pseudocode.

💻 04 Code

Implement Q × K from scratch in Python. Keep it minimal — just enough to see it work.

🔬 05 Experiment

Try Q × K on a real example. Vary the inputs and observe what changes.

⚖️ 06 Compare

How is Q × K different from related concepts in QKV? What are the trade-offs?

🗣️ 07 Explain

Explain Q × K in plain language using an analogy. Could a non-expert follow it?

🔨 08 Build

Build a small project or demo that uses Q × K as a core component.

← Value Scaling →

🧪

RASL Pull · S-04 Pulled because: why does the verifier weight some evidence higher?
🔨 M-05 attention.py 🔁 Q/K/V intuition → verifier scoring → return to agent/verifier.py 📋 full plan →

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