Evaluating LLMs for Office Work: Research, Reports, and Persuasion

Why Office Tasks Need Their Own Evaluation

Generic benchmark scores reflect a model’s overall ability, but they rarely answer a concrete question: can this model help me write a weekly report that is both accurate and concise? Office tasks have three defining characteristics. First, the input is usually incomplete—the model needs to fill in context or judge which information matters. Second, the output serves a specific audience: managers care about conclusions, colleagues care about supporting evidence. Third, the cost of errors is uneven—a single factual mistake can undermine the credibility of an entire document.

Therefore, evaluating models for office scenarios should start from real workflows rather than public leaderboards. Interviewing frontline users and collecting the requests they encounter most often is a sensible starting point for building an evaluation set.

Breaking Down Three Typical Task Types

1) Information Retrieval and Summarization

The core requirements here are accuracy, traceability, and no fabrication. Common requests include summarizing policy changes in an industry, extracting key points from a long report, or comparing the pros and cons of two technical approaches.

When evaluating, focus on:

• Whether the model distinguishes confirmed facts from speculation.
• Whether it explicitly states uncertainty when sources are insufficient, instead of producing plausible-sounding answers.
• Whether cited numbers, dates, and names can be traced back to the original material.
• If the model supports web search, whether results cite sources and timestamps.

2) Argumentation and Persuasion

Users often need to package a judgment into a persuasive recommendation for management—say, securing budget or driving a process change. A good output leads with the conclusion, follows with evidence, and anticipates objections.

Key evaluation points: whether claims correspond to facts, whether there is circular reasoning or concept-switching, and whether the framing is adapted to the audience’s concerns. Models tend to produce confident-sounding but weakly supported text—evaluation should specifically check for this.

3) Structured Text Generation

Weekly reports, meeting minutes, and requirement documents are structured text. These tasks demand high format compliance and information completeness, plus concise language.

You can check: whether all key items provided by the user are included, whether risks and action items are missed, whether the structure matches the team template, and whether anything contradicts the input.

Building the Evaluation Set

The quality of the evaluation set determines how trustworthy your conclusions are. Build it in these steps:

Step 1: Collect real samples. Gather actual requests from frontline users over a recent period. Strip sensitive content such as names, client information, and internal codenames while preserving task structure.

Step 2: Stratify by task type. Each task type should have enough samples and cover easy, medium, and hard difficulty levels. If your samples only include simple questions, the evaluation will overestimate model capability.

Step 3: Design scoring criteria for each sample. Open-ended outputs are hard to judge against a single correct answer, so write checklists instead. For a weekly report task, list “must-include items” and “must-not-appear phrases” and check each one.

Step 4: Split into a development set and a validation set. Use the development set while tuning prompts, and report final results only on the validation set to avoid overfitting.

Scoring Dimensions and Weights

Cover at least five dimensions:

• Correctness: Are facts, numbers, and dates accurate?
• Completeness: Does the output cover all required points?
• Verifiability: Are key conclusions backed by evidence or sources?
• Writing quality: Is the structure clear, the language concise, and the tone appropriate for the reader?
• Efficiency and cost: Response latency, per-call cost, and failure rate.

Weights should be set by the team based on the scenario. For external materials, correctness and verifiability deserve higher weights; for internal reports, completeness and writing quality may matter more. Once set, weights must remain unchanged throughout the evaluation.

Combining Automation with Human Review

Relying entirely on human review is costly and slow, while pure keyword matching easily misjudges. A practical approach combines both: automate the mechanical checks and let humans handle semantic judgment.

Below is a generic evaluation skeleton. The model invocation part is left for your team to implement and is vendor-neutral.

from dataclasses import dataclass, field
from typing import Callable
@dataclass
class EvalCase:
    task_type: str                 # retrieval / persuasion / report
    prompt: str
    must_include: list[str] = field(default_factory=list)
    must_not_include: list[str] = field(default_factory=list)
def score_response(case: EvalCase, answer: str) -> dict:
    hit = sum(1 for kw in case.must_include if kw in answer)
    coverage = hit / len(case.must_include) if case.must_include else 1.0
    violations = [kw for kw in case.must_not_include if kw in answer]
    return {
        "task_type": case.task_type,
        "coverage": round(coverage, 2),
        "violations": violations,
    }
def run_suite(cases: list[EvalCase], model_fn: Callable[[str], str], runs: int = 3):
    results = []
    for case in cases:
        for _ in range(runs):          # Multiple samples per case to reduce randomness
            answer = model_fn(case.prompt)
            results.append(score_response(case, answer))
    return results

Keyword checks only cover part of the requirements. They work well for “must-appear items” and “forbidden phrases,” but argumentation quality and tone still need human spot-checks. Randomly sample a fixed percentage of each batch, have two reviewers score independently, and record cases with large disagreements to refine the scoring criteria.

Common Pitfalls in Interpreting Results

Pitfall 1: Treating a single run as a stable conclusion. LLM output is stochastic—the same prompt can yield noticeably different results across runs. Sample multiple times and report means and variance.

Pitfall 2: Ignoring prompt effects. The same model can perform very differently depending on how well the prompt is written. Fix the prompt template during evaluation and document the template as part of the evaluation itself.

Pitfall 3: Drawing conclusions from tiny sample sizes. Good performance on a few samples does not indicate overall capability. Long-tail office needs in particular require enough samples to expose problems.

Pitfall 4: Treating overall rankings as a selection criterion. Rankings cannot reflect your team’s data security requirements, integration costs with existing systems, or usage habits. Selection should return to specific tasks: which option reaches an acceptable level on the metrics that matter.

Practical Recommendations

First, establish an internal evaluation baseline. After every model version change or prompt template update, re-run the same sample set and watch how metrics move.

Second, define data boundaries. Both evaluation and daily use must comply with your organization’s data policies—sensitive material should never be fed into external services without approval.

Third, convert evaluation results into usage guidelines. For example, require a source-verification step for research tasks, and human confirmation of numbers in generated reports. These rules reduce risk more effectively than simply comparing models.

Fourth, keep the evaluation set current. Office needs evolve with the business—add new samples and retire outdated ones quarterly so the evaluation keeps reflecting real work.

In short, evaluating LLMs for office scenarios doesn’t require a complex theoretical framework. The keys are breaking down real tasks clearly, writing concrete scoring criteria, and validating results back in the actual workflow. Conclusions reached this way can directly guide your team’s tool choices and usage standards.