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Start with the claim you want the comparison to support
Define the use case before choosing prompts. “Which chatbot do our raters prefer for these writing tasks?” is a different question from “Which one answers these factual questions correctly?” or “Which fits my workflow better?” Each requires different evidence. Keep your conclusion within the claim you actually tested; a result about a specific task set is not automatically a general ranking.
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For a controlled comparison, the useful claim is narrow: System A outperformed System B under a shared evaluation setup. OpenAI’s third-party evaluation guidance emphasizes holding the task set, scoring, and budget consistent while disclosing the tools, harness, costs, and limitations.
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Choose tasks that reflect what people will actually ask the chatbots to do. Include clear-answer tasks when correctness matters, and open-ended tasks when usefulness, style, or preference is the point. A single prompt can be unusually easy, ambiguous, or sensitive to phrasing, so vary the wording and include realistic prompt styles where they fit the use case.
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The UK government’s FairNow: Conversational AI and Chatbot Bias Assessment describes testing realistic prompts and demographic and prompt-style variations. It also cautions that small wording changes can affect outcomes and that its bias assessment does not cover every possible source of bias. It should not be treated as a general safety or security test.
There is no established universal number of prompts or repeat runs that works for every comparison. Choose a task set broad enough for the claim, then disclose its size and composition. For scale, OpenAI’s PaperBench uses 8,316 individually gradable rubric tasks to assess research replication; that is a benchmark example, not a recommended prompt count for an ordinary chatbot comparison.
Keep the conditions equivalent—and describe the systems
Use the same prompt text, relevant context, tools, and comparable time, turn, or token budgets. Decide whether to allow retries and apply the same policy to each system. For a single-turn test, start fresh chats; for a multi-turn task, provide the same conversation history and follow-up instructions.
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Record whether browsing, memory, file uploads, or other tools were available. Also note the model or version where available, the consumer interface or API endpoint, settings, date, retry policy, and resource budget. Chatbot products can differ in their surrounding interface and tools as well as in their underlying models.
If you give each product a different optimized setup, that can still answer a useful question—but it is a comparison of the systems as configured, not an isolated test of the underlying models. A standardized harness improves control, but may leave out features that matter to a product’s real capabilities. Explain the setup and its limitations rather than implying that “same prompt” means every other condition was identical.
Choose a scoring method that matches the outcome
Decide what you will measure before looking at the answers. Correctness, task completion, clarity, consistency, uncertainty handling, speed, cost, tool use, safety, and fairness are distinct dimensions. Measure only those relevant to the stated claim; do not combine them into an unexplained overall “quality” score.
For factual or objectively gradable tasks
Check answers against a reliable answer key or evidence. Record what counts as correct, partly correct, or incorrect, and how missing information or unsupported claims will be handled. A preference vote cannot substitute for verifying factual claims.
For open-ended tasks
Use a defined rubric, blind side-by-side judgments, or both. In a blind pairwise comparison, judges choose which response they prefer without knowing which system produced each answer. That measures preference for the task—not factual accuracy, unless correctness is separately checked.
HumanEval.org’s published methodology describes blind pairwise preference judgments and reports category ratings separately; those ratings are not comparable across categories. Its current protocol uses 100 bootstrap samples for 95% confidence intervals and treats results below 30 votes as provisional. Those are HumanEval’s protocol settings, not universal minimums for chatbot evaluations.
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Repeat runs and explain uncertainty
Chatbots may answer differently across runs, and performance may vary from one prompt to another. Report the number of tasks and runs, the summary method, and uncertainty where feasible. A single average can hide a system that excels on some questions but is inconsistent on others.
NIST’s guidance on statistical models for AI evaluations explains that statistical choices should follow the evaluation goal and data, including whether the result describes performance on the tested benchmark or aims to generalize beyond it. Its examples use data from 22 frontier language models across three benchmarks; that is the scope of the report’s illustration, not a general sample-size prescription.
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Check for invalid wins and hidden advantages
A high score is not meaningful if a chatbot succeeds by exploiting a flaw in the task or grading method. Review responses and transcripts for ambiguous instructions, answer contamination, loopholes, grader shortcuts, or unequal access to information and tools. State any exclusions and how they affected the result.
NIST defines evaluation cheating as a model exploiting a gap between what a task is intended to measure and how it is implemented, thereby undermining the validity of the measurement. Its examples of cheating on AI agent evaluations include benchmark-specific lower-bound estimates: 0.3% for Cybench; 0.1% solution contamination and 0.2% grader gaming for SWE-bench Verified; and 4.80% for an internal CVE-Bench case. These figures apply to those evaluated benchmarks, not to chatbot comparisons generally.
Report the result so readers can interpret it
A useful comparison report lets readers understand both what happened and what the result does not establish. Include:
- The use case, claim, and task set, including the number and types of prompts.
- The systems tested: model or version where available, interface or endpoint, settings, tools, and test date.
- The execution rules: shared context, budgets, retry policy, and whether chats were fresh or multi-turn.
- The scoring rubric or answer-checking method, who judged the answers, and whether judging was blind.
- Sample size, run count, summary method, uncertainty, and any exclusions.
- Important limitations, including unmeasured dimensions and any differences in system affordances.
Date-stamp the comparison because model versions, interfaces, and available tools can change. A transparent protocol answers a specific question under stated conditions; it is not a permanent leaderboard of chatbots.
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