A Lightweight Evaluation Plan for AI Writing Assistants

Teams often adopt an AI writing assistant because it feels faster than starting from a blank page. The problem is that “faster” is not the same as “better” and quality can drift in subtle ways: slightly wrong facts, off-brand tone, or confident answers that should have been cautious.

You do not need a research lab to prevent this. What you need is a lightweight evaluation plan that fits into normal work, produces repeatable signals, and helps you make decisions like “ship this prompt change,” “switch models,” or “keep humans in the loop for this workflow.”

This post lays out a small-team approach: a tiny test set, a simple rubric, and a short review loop. The goal is not perfect measurement. The goal is dependable improvement.

Why evaluate a writing assistant at all?

Without evaluation, teams usually rely on vibes: a few people try the tool, it seems fine, and then it gets embedded in processes. Weeks later someone notices that customer emails have become longer, content is less accurate, or internal docs are harder to scan. By then, it is difficult to pinpoint what changed.

A lightweight eval gives you three practical benefits:

• Change safety: when you tweak prompts, templates, tone rules, or routing logic, you can check that you did not break important cases.
• Clear tradeoffs: you can see where the assistant saves time versus where it increases risk (for example, legal claims, pricing, or commitments).
• Shared expectations: writers, support agents, and reviewers align on what “good output” looks like.

Define scope: what “good” looks like

Evaluation only works when you specify the job. “Write better” is too vague. Start by listing the assistant’s top 1 to 3 use cases and the constraints that matter most.

Two minutes to define your target

Write down answers to these questions in plain language:

• Audience: who reads the output (customers, leads, internal teammates)?
• Output type: email reply, knowledge base article, product description, meeting summary, social caption?
• Risk level: what is the worst plausible harm (misleading advice, privacy leak, reputational damage)?
• Sources of truth: where should facts come from (internal docs, templates, a product catalog)?
• Non-negotiables: must not invent policy, must not promise timelines, must keep a friendly tone, must not mention internal tooling.

Pick one use case to evaluate first. A narrow scope makes your results clearer and your improvements faster.

Build a small, reusable test set

A test set is a collection of realistic inputs that you run through the assistant again and again. You want it small enough to maintain, but diverse enough to catch regressions.

For a small team, aim for 12 to 20 test cases. Each test case should include:

• Input: the user request or the raw material (a customer email, bullet notes, a product spec).
• Context: any information the assistant should rely on (brand voice rules, policy excerpt, product details).
• Expected behavior: not a perfect answer, but what must be true (no pricing promises, ask a clarifying question, include steps, cite the internal policy snippet).

Balance your set across these buckets:

• Happy path: straightforward requests that should be easy.
• Edge cases: ambiguity, missing info, conflicting constraints, or sensitive topics.
• Policy traps: requests that tempt the model to make up facts or exceed what your company allows.

If you are worried about time, start with 10 cases. It is better to run an eval monthly than to build a “perfect” dataset that no one maintains.

Use a simple rubric you can score consistently

A rubric turns subjective review into a repeatable practice. It also forces you to separate “I do not like this phrasing” from “this is risky or wrong.” Keep the rubric short enough that two reviewers can finish scoring quickly.

Rubric dimensions that work for writing assistants

1. Correctness: factual claims align with provided context. No invented details.
2. Task completion: the response actually answers the request and includes necessary steps, options, or next actions.
3. Clarity: organized, scannable, and not bloated. Uses bullets where appropriate.
4. Tone and brand fit: matches your voice guidelines and avoids prohibited phrasing.
5. Safety and policy: avoids commitments, sensitive data, or disallowed guidance. Uses disclaimers or escalation when needed.

Score each dimension on a simple 0 to 2 scale:

• 2: solid, would send or publish with minimal edits
• 1: usable but needs meaningful edits
• 0: unacceptable, risky, or off-task

If you only track one metric, track “0 scores per run”. A single unacceptable output in a high-risk workflow matters more than small average improvements.

{
  "test_case_id": "KB-07",
  "input": "Customer asks if the Basic plan includes SSO.",
  "context": "Pricing table excerpt + policy: do not promise roadmap.",
  "output": "(assistant response here)",
  "scores": { "correctness": 0, "task_completion": 1, "clarity": 2, "tone": 2, "policy": 1 },
  "notes": "Invented that Basic includes SSO. Should say not included and offer upgrade path."
}

Run the evaluation in 60 to 90 minutes

You can run this eval with two people and a shared spreadsheet. The key is consistency: same test set, same rubric, and a short debrief that produces an action list.

1. Freeze the setup: note the model, prompt, templates, and any context injection rules. If you cannot describe the setup, you cannot compare runs.
2. Generate outputs: run all test cases through the assistant. Save outputs exactly as produced.
3. Blind review (optional but helpful): if you are comparing two prompt versions, label outputs “A” and “B” so reviewers do not know which is which.
4. Score quickly: reviewers score independently, then reconcile only the 0s and big disagreements.
5. Summarize: count 0s per dimension, list recurring failure modes, and pick the top 1 to 3 fixes.

What to do with results:

• If 0s increased, roll back the change or restrict the assistant’s usage until fixed.
• If correctness is weak, tighten context, reduce open-ended generation, and require citations to provided snippets.
• If clarity is weak, add formatting rules (bullets, short paragraphs) and explicit length guidance.
• If policy failures appear, add refusal or escalation patterns and remove temptations (like “be decisive” prompts).

A concrete example: support macros for a SaaS team

Imagine a five-person SaaS support team that uses an AI assistant to draft first replies. Their goal is to reduce typing, while keeping replies accurate and consistent with policy.

They pick one use case: “Draft a reply to common billing and account questions.” They build a 15-case test set:

• 5 happy-path billing questions (refund window, invoice receipt, updating card).
• 5 edge cases (customer angry, unclear request, multiple issues in one email).
• 5 policy traps (asks for a refund outside window, requests account data for another user, asks for a discount).

In their first eval, they find three recurring problems:

• The assistant apologizes and then offers refunds that violate policy.
• It gives long explanations instead of short next steps.
• It sometimes asks for unnecessary personal information.

They make two changes: (1) include a short policy excerpt in the context, and (2) add a response structure rule: “Answer in 3 parts: acknowledgement, decision with policy basis, next step.” In the next run, policy 0s drop to zero, and clarity improves, even though tone becomes slightly less warm. The team accepts that tradeoff and later revisits tone with more precise examples.

Common mistakes (and how to avoid them)

• Testing only easy prompts: include uncomfortable cases that create pressure to hallucinate or overpromise.
• Changing multiple variables at once: if you switch model and rewrite prompts and add new context, you cannot attribute improvements.
• Over-scoring style preferences: make “tone” a rubric dimension, but do not punish harmless variations unless your brand truly requires consistency.
• Ignoring “unknown” handling: explicitly test whether the assistant asks clarifying questions or escalates when context is missing.
• Not recording inputs and context: outputs are not enough. The same input with different context rules can behave very differently.

When not to do this

Lightweight evaluation works best when your assistant has a stable job and produces repeatable outputs. It is a poor fit when:

• The task is highly creative by design: for example, brainstorming taglines where diversity is the goal. You can still evaluate, but your rubric should focus on constraints and safety, not “best” phrasing.
• You have no source of truth: if the assistant must be correct but you cannot provide accurate context, an eval will simply confirm that the system is unreliable.
• Risk is high and stakes are serious: in those cases, keep humans as primary authors and use AI for narrow drafting or reformatting tasks, with strict review.

Conclusion

You do not need complex infrastructure to evaluate an AI writing assistant. A small test set, a consistent rubric, and an hour of focused review can keep quality stable while you iterate. Over time, this becomes a shared language for what your team expects from AI output and where humans must stay in control.

FAQ

How often should we run this evaluation?

Run it whenever you change prompts, templates, model settings, or context sources. If your setup is stable, a monthly or quarterly run is usually enough to catch drift in process and expectations.

Do we need multiple reviewers?

Two reviewers is ideal for consistency, but one careful reviewer can work if time is tight. If you use one reviewer, be stricter about writing down scoring notes so your future self scores the same way.

What if our outputs vary a lot from run to run?

Variation is normal for generative systems. Reduce randomness where possible (for example, consistent templates and constraints), and score behaviors that should be stable: correctness, policy compliance, and whether it asks clarifying questions when context is missing.

Should we pick the model with the best average score?

Not always. In many workflows, the worst-case failures matter more than the average. Prefer the option with fewer 0s in high-risk dimensions like correctness and policy, even if average tone or style is slightly lower.