Tokens are the basic accounting unit used by large language models. When you send a prompt, the model does not read “words” the way humans do; it reads tokenized fragments, which may be whole words, partial words, punctuation, or whitespace patterns. API providers bill by token usage, so every extra instruction, repeated phrase, and verbose sentence increases cost. This matters at scale: a prompt that is only 100 tokens longer can add millions of paid tokens over a month in production.

FruKal uses Llama 3.3 70B as the optimization engine because it balances quality and efficiency for rewrite tasks. In practice, prompt compression needs both semantic preservation and structural editing: remove redundancy, merge overlapping instructions, and preserve required constraints. Smaller models may over-compress and drop intent; slower models can hurt UX. Llama 3.3 70B is a strong middle path for high-fidelity rewriting with fast enough turnaround for interactive use.

Optimization can also improve answer reliability. Hallucinations often increase when prompts are ambiguous, contradictory, or overly broad. A refined prompt adds clear boundaries: objective, context, constraints, and output schema. Instead of “write a report,” a better prompt asks for “5 bullet insights using only provided facts, then list assumptions separately.” This structure lowers ambiguity and nudges the model toward grounded responses. So optimization is not just about saving money—it is about better controllability and more consistent outputs.