3 Edits That Stop AI Word Salad Before You Send It

You open a draft from your AI assistant. It reads smoothly. It sounds confident. And yet, after three paragraphs, you still have no idea what it wants you to do. The language is polished but empty. This is AI word salad: grammatically correct, logically hollow. It wastes time, erodes trust and buries the decisions your team actually needs to make. The fix is not a better prompt. It is three editing moves you can apply in under five minutes.

Why this matters at work

Teams that rely on AI for internal updates, project summaries or client emails often assume the output is nearly ready. The model has done the heavy lifting. A quick read confirms the tone is professional. So they send it. What comes back is a trail of follow-up questions: What exactly do you mean by ‘leverage our core strengths’? Which team is responsible? When is the deadline?

The cost is not just time. It is credibility. When every AI-generated message sounds the same, readers stop reading. They skim for the one concrete detail and, if they cannot find it, they move on. In a workplace where decisions depend on clear communication, vague AI drafts become a liability. Misunderstandings lead to missed deadlines, duplicated work and frustrated colleagues.

There is also a risk management angle. If your AI draft makes a claim about performance, budget or compliance, and you send it without verification, you are responsible for the error. The model does not know your data. It guesses. A vague sentence like ‘We have seen significant improvement’ is not just unhelpful. It is potentially misleading if the actual numbers tell a different story.

The good news is that most AI word salad follows a pattern. It uses filler phrases, generic nouns and weak calls to action. Once you know what to look for, you can strip it out and leave only what matters. The three edits below cover the majority of cases. They are not about rewriting from scratch. They are about cutting, specifying and tightening until the message is impossible to misunderstand.

Edit one: cut filler

AI models are trained to sound fluent. That fluency often comes from padding: words and phrases that add rhythm but no meaning. ‘In order to’ instead of ‘to’. ‘In the event that’ instead of ‘if’. ‘At this point in time’ instead of ‘now’. These are not errors. They are habits the model learned from a corpus of polite, cautious business writing. Your job is to delete them.

Start by scanning for the most common filler phrases. ‘It is important to note that’ can almost always be removed. The reader will decide what is important. ‘We are pleased to announce’ is often unnecessary in an internal update. Just announce it. ‘As you may be aware’ assumes the reader might not be aware, which is fine, but the phrase itself adds nothing. Cut it.

Next, look for redundant modifiers. ‘Very unique’ is redundant because unique already means one of a kind. ‘Absolutely essential’ is redundant because essential already means necessary. ‘Completely finished’ is redundant because finished means done. The model does not know these are redundant. It strings together intensifiers because they appear in training data. You know better.

Finally, watch for sentences that restate the obvious. ‘The purpose of this email is to inform you that the meeting has been rescheduled.’ The purpose is obvious from the context. You can open with ‘The Tuesday stand-up has moved to 10am.’ That is cleaner and faster.

A practical test: read your draft aloud. If you stumble over a phrase or find yourself skipping it mentally, that phrase is filler. Cut it. The sentence will still make sense. In most cases, it will make more sense because the core idea is no longer buried.

One caveat: do not cut so aggressively that you lose tone or politeness. A short message can still be courteous. ‘Thanks for your patience’ is not filler. ‘Please review the attached’ is not filler. The goal is to remove words that do no work, not to make every sentence a telegram.

Edit two: add specific nouns and numbers

AI drafts are vague by default. The model does not know your specific project, your team members or your budget. It fills the gaps with generic nouns: ‘stakeholders’, ‘resources’, ‘optimisation’, ‘synergy’, ‘alignment’. These words are not wrong. They are just empty. They let the reader fill in their own meaning, which is almost never the meaning you intended.

The fix is to replace every generic noun with a specific one. Instead of ‘stakeholders’, say ‘the product team and the legal department’. Instead of ‘resources’, say ‘the design budget and the two developer hours allocated to this sprint’. Instead of ‘optimisation’, say ‘reducing page load time from 4.2 seconds to under 2 seconds’.

Numbers are your best friend. ‘We improved efficiency’ is a claim. ‘We reduced processing time by 23 per cent over six weeks’ is evidence. The model will not invent numbers because it does not know them. You must supply them. If you do not have the exact figure yet, say ‘we expect a reduction of roughly 15 per cent, pending the final test run’. That is still better than a vague claim because it gives the reader a benchmark to track.

Be careful with percentages and absolutes. ‘Everyone agreed’ is almost never true. ‘The majority of the team agreed’ is more honest. ‘We always do it this way’ is a red flag. ‘We have used this process for the last three quarters’ is verifiable. Specificity builds trust. Vagueness invites scepticism.

This edit also applies to dates and deadlines. ‘We will update you soon’ is useless. ‘We will share the revised timeline by Friday 14 March’ is actionable. If you cannot commit to a specific date, say why. ‘We are waiting for vendor confirmation and will update you within 48 hours of receiving it.’ That tells the reader what to expect and why the delay exists.

Edit three: tighten calls to action

The most common failure in AI-written workplace messages is a weak or missing call to action. The draft ends with ‘Let me know if you have any questions’ or ‘Looking forward to your feedback’. These are polite but passive. They put the burden on the reader to figure out what to do next. Most readers will do nothing.

A strong call to action tells the reader exactly what action is needed, who should take it and by when. ‘Please review the attached budget and approve it by Wednesday 19 March’ is clear. ‘If you have concerns, reply to this email by Friday with specific line items you want to discuss’ is even better because it narrows the response.

If the action is not for everyone, say so. ‘No action needed from the design team at this stage. The engineering team should confirm the API endpoints by Tuesday.’ That saves the design team from reading a message that does not apply to them, and it gives the engineering team a clear task.

Watch for hidden calls to action disguised as questions. ‘Could you take a look at the report?’ is a request, but it is soft. ‘Please review the report and send your edits by end of day Thursday’ is firm. If you are not the authority to make it firm, at least be specific. ‘When you have a moment, could you review the report and let me know if the assumptions on page 3 look reasonable?’ That is polite but still directs attention to a specific part of the document.

One more trap: the ‘let me know if you have questions’ close. It is fine for a purely informational message. But if you need a decision, a confirmation or an input, state it directly. The reader should never have to guess what you want from them.

A quick before and after example

Before: a vague AI draft

‘We are pleased to announce that we have been working diligently to optimise our internal processes in order to better serve our stakeholders. As a result of these efforts, we have seen significant improvements in overall efficiency and collaboration across teams. We believe that this will lead to enhanced outcomes in the coming quarters. Please do not hesitate to reach out if you have any questions or require further clarification.’

After: the edited version

‘We have cut the time to approve a purchase order from five days to two days. This change affects the finance team and the procurement team. Starting next Monday, use the new approval form in the shared drive. If you have questions about the form, reply to this email by Thursday 20 March.’

What changed? The filler is gone. ‘Working diligently’ and ‘optimise our internal processes’ are replaced with a concrete result: cutting approval time. ‘Stakeholders’ becomes ‘the finance team and the procurement team’. The vague claim about ‘significant improvements’ becomes a specific number: five days to two days. The weak call to action ‘do not hesitate to reach out’ becomes a clear instruction with a deadline.

The edited version is shorter, more honest and more useful. It tells the reader what happened, who is affected, what to do and by when. That is the whole job of a workplace update.

What to check before you send

Before you hit send on any AI-assisted draft, run through this checklist. It takes less than a minute and catches the most common problems.

• Check for filler phrases. Search for ‘in order to’, ‘it is important to note’, ‘as you may be aware’, ‘at this point in time’. Delete them. The sentence will read better without them.
• Check for generic nouns. Replace ‘stakeholders’, ‘resources’, ‘optimisation’, ‘synergy’ with specific names, teams, numbers or outcomes. If you cannot name them, you do not have enough information to send the message yet.
• Check for missing numbers. Any claim about improvement, change or impact should have a number attached. If the number is not ready, say when it will be ready.
• Check the call to action. Is it clear who should do what and by when? If the answer is ‘let me know if you have questions’, consider whether you actually need a response. If you do, rewrite it.
• Check for unsupported claims. Did the AI write something like ‘this approach has been proven to work’ or ‘industry leaders agree’? Verify it. If you cannot verify it, delete it or qualify it with ‘according to our internal test results’ or similar.
• Check the tone. AI drafts can sound overly enthusiastic or falsely humble. Read the message as if you were the recipient. Would you take it seriously? If it sounds like a press release, dial it back.

One final check: read the message without the first paragraph. If the core point is still clear, the first paragraph was probably filler. Cut it and start with the second paragraph instead.

What the reader can take away

AI word salad is not inevitable. It is a symptom of using the model without editing. The three edits cut filler, add specific nouns and numbers, and tighten calls to action. They do not require special training or a new tool. They require a few minutes of attention before you send.

The payoff is immediate. Your messages become shorter, clearer and more trustworthy. Readers stop asking follow-up questions because the answers are already in the text. Decisions move faster because everyone knows what to do. And you avoid the risk of sending a vague claim that someone later interprets as a commitment.

Next time you open an AI draft, do not read it for approval. Read it for editing. Cut the padding. Name the people and the numbers. Tell the reader exactly what to do. The model gave you a starting point. You give it the meaning.

Make the review habit stick

The safest habit is to review an AI draft before it becomes part of the team’s record. The reader can test the draft against three questions: what decision does this support, what facts would a colleague need to check, and what action should happen next. If the answer is unclear, the draft is not ready, even if every sentence sounds polished.

This is why AI word salad is a workplace risk rather than just a writing problem. Vague updates create follow-up meetings, hidden assumptions and soft commitments that nobody meant to make. A better routine is to keep the source notes beside the draft, mark any fact that must be checked, and then rewrite the message so ownership, dates and next steps are explicit.

For a broader review habit, pair this method with Cristoniq’s guide to how to check an AI draft before sending. If the draft came from a long report or policy document, use the same caution described in AI document summarisation: go back to the original for important claims, numbers and commitments.

AI word salad source note

Teams should also treat AI word salad as a governance signal. The UK Information Commissioner’s Office AI guidance is a useful reminder that AI outputs need human accountability, documented decisions and proportionate checks. In everyday workplace writing, that starts with making vague AI-generated claims specific enough for someone to verify.