Why does the ORDER of few-shot examples in a prompt measurably affect output quality and where should the most-representative example go?

**Few-shot example order** matters because LLMs read left to right and the last example sits right next to where the model starts generating. The correct answer is B. This is recency bias. Attention is global but practical attention weight skews toward nearby tokens, and the tokens immediately before the model's first generation step are the ones with the strongest pull on the output distribution. The practical rule has two parts. First, put your most-representative example last. If you have five examples and one is the canonical case (typical input, ideal output, the pattern you want the model to imitate), that one belongs in the last slot, not the first. Second, avoid style oscillation. If you alternate verbose, concise, verbose, concise, the model often averages or flips between styles. Keep the style direction consistent across examples and let the last example anchor it. The other options are common traps. Option A repeats the folklore that 'examples are a set'; multiple papers measured the effect size at 5 to 15 points on benchmarks, which is huge. Option C orders by length; length is not the right axis. Option D orders alphabetically; that is arbitrary and does not connect to where the model generates from.

**Few-shot order** matters because of recency bias: the model generates right after the last example, and that example primes the next-token distribution most strongly. Answer B. The rule is to put the most-representative example last so the cleanest pattern is the one immediately adjacent to the model's generation cursor. The second rule is to avoid style oscillation; alternating contradictory examples (long, short, long, short, or formal, casual, formal, casual) causes the model to average or flip between modes. Measured order effects on standard benchmarks land in the 5 to 15 point range, which is enough to flip a regression test. That makes example order a real hyperparameter, not an aesthetic choice. The other options miss the mechanism. Order matters (A is wrong). Length is not the axis (C is wrong). Alphabetical order is arbitrary relative to the position that matters, which is 'distance from the query' (D is wrong). The only ordering that connects to how the model uses examples is by representativeness, last slot first.