How does it keep the comparison fair?

The prompt instructs the model to apply the same standard to every item, mark unknown cells as "unknown" rather than guessing, separate facts from opinion, and watch for recency, popularity, and order biases.

What is dimension weighting?

When you choose importance-weighting, the prompt asks the model to assign each dimension a weight reflecting how much it should matter, then compute a weighted total — so a minor factor cannot swing the verdict like a major one.

Why does it ask for a "Best for…" line per item?

"Best" depends on context. By giving each item a one-line "Best for…" recommendation, the analysis lets different readers self-select rather than forcing a single winner that may not fit their situation.

Can the model compare things it does not have data on?

Only partially. The prompt tells it to mark missing information as unknown instead of inventing it. For current prices or specs, verify the output against primary sources, since model knowledge can be stale.

Comparative Analysis Prompt Builder

Comparative analysis prompt builder

Asking an LLM to “compare X and Y” usually yields a lopsided, hand-wavy answer that quietly favors whatever it has seen most. This tool builds a prompt that forces a rigorous, even-handed comparison: explicit dimensions, optional weighting, bias-avoidance rules, and a structured output you can act on. It is assembled locally in your browser.

How it works

You list the items to compare and, optionally, the dimensions to evaluate them on — or let the model pick the decision-relevant ones and justify them. You choose equal weighting or importance weighting, and an output format: comparison table, pros and cons, weighted scorecard, or narrative. The builder produces a prompt that tells the model to apply one consistent standard across all items, mark unknowns honestly, separate fact from opinion, and finish with a context-specific “Best for…” line per item.

Tips and notes

Choose dimensions that actually drive your decision; a comparison on irrelevant axes looks thorough but does not help you choose. Importance-weighting is worth it when the factors clearly differ in significance — for example, price might matter far more than a minor feature. Because model knowledge can be out of date, always verify time-sensitive cells like current pricing or version numbers against primary sources before relying on the verdict. The per-item “Best for…” output is especially useful when you are sharing the analysis with others who may weigh the trade-offs differently than you do.