Predict whether tokenizer.encode('world') and tokenizer.encode(' world') produce the same token IDs in tiktoken.

The two calls produce **different** token IDs. The reason is that byte-level BPE, which `cl100k_base` uses, treats the leading space as part of the token. Here is the intuition. BPE builds its vocabulary from the raw byte sequences that show up in training text. The strings `world` and ` world` (with a space) are literally different byte sequences. In normal English, a word almost always appears after a space, so the pattern ` world` is extremely common, and the tokenizer learns it as its own single token with its own ID. The bare `world`, with no leading space, is rarer (it mostly happens at the very start of a string) and gets a different ID. So running the code, you would see something like `[14957]` for `world` and `[1917]` for ` world`. Same letters, different IDs. This is not a bug; it is how the tokenizer encodes word boundaries. But it does bite people who build prompts by gluing strings together. If you forget a space, the first word of the next piece gets the wrong token, and the model sees something subtly different from natural text.

**Different token IDs.** In byte-level BPE, which `cl100k_base` uses, the leading space is folded into the token, so ` world` and `world` are separate vocabulary entries. The mechanism: BPE learns merges from contiguous byte spans in training data. Because nearly every word in English prose appears after a space, the tokenizer learns the space-prefixed form ` world` as a single common token. The bare `world` is the rarer position-zero form and maps to a different ID. Concretely you would see roughly `[14957]` for `world` and `[1917]` for ` world`. This is deliberate. Folding the space into the token is how byte-level BPE represents word boundaries without a separate boundary symbol; it is why decoding round-trips spacing correctly. The practical consequence is the trap behind the question. If you build prompts by concatenating strings and drop a space, the first token of the next segment shifts to its no leading space form. That differs from what the model saw during training on naturally spaced text. The safe habit is to let `apply_chat_template` or the tokenizer handle spacing rather than hand-gluing role labels and content.