What is Dijkstra’s algorithm limitation?
PICTURE THIS: AN LLM TURN
Text inTokens
TransformerAttention
Text outNext token
Simple meaning
It needs non-negative weights.
WHY — Graphs instead of guessing?
Why interviewers care about Graphs:
Graphs questions separate people
who only read docs from people who shipped.
Keep it short, concrete,
and tied to DSA work.
Stay structured
Name the idea, why it exists, then one short example.
Close cleanly
End with when you use it and one common pitfall.
STEPS — What happens with tokens?
Before the model can read a sentence, it goes through these steps:
- 1Tokenization
It needs non-negative weights.
- 2Token IDs
Negative edges need Bellman-Ford.
- 3I state complexity with
heap implementations.
- 4Context mix
Attention looks at nearby tokens together.
- 5Next token
The model scores what should come next.
- 6Decode
IDs turn back into readable text.
EXAMPLE — See it in action
Let's see how a real sentence is tokenized (tokens may vary by model):
Input text
“Negative edges need Bellman-Ford.”
Tokenized output
NegativeedgesneedBellmanFord
Token IDs (example)
298740833747163290
Note: Actual tokens and IDs depend on the tokenizer (e.g., GPT, Llama, etc.).
Key takeaway
It needs non-negative weights. Negative edges need Bellman-Ford.