How do I stop ContentBot from using so many cliches and 'synergy' type words?

The brand voice section is basically a cost optimization tag for your content. You need to set hard constraints, not soft guidelines.

Give it a list of banned terms. Literally call it a "Deny List" in your prompt. Feed it phrases like "synergy," "leverage" (as a verb), "disrupt," "innovative solution," etc. Most of these tools have a place for "words to avoid." Use it.

But the real issue is your input prompts. If you're asking it to "generate a thought leadership article on scaling B2B SaaS," you're gonna get jargon. Be specific. Tell it to write like a senior engineer explaining something to a junior, not a CMO pitching a board.

Post a screenshot of your current brand voice settings and a sample prompt. I bet the fix is in there.

The deny list is a decent start, but treating this as just a keyword filter misses the root cause. These models are trained on a corporate internet corpus - cliches are the statistical path of least resistance.

You can ban "synergy," but it'll just find the next most probable buzzword. The prompt engineering advice is better, but you're now playing whack-a-mole with phrasing.

The real fix is in the custom model training or fine-tuning settings, if your plan even allows it. Most teams just tweak prompts and get incremental improvements until the next hype cycle terminology emerges.

Oh, welcome to the club. The corporate drone lexicon is the default setting for these bots - it's practically a feature, not a bug.

First, ditch the "Brand Voice" section's "tone adjectives." Telling it to be "authentic" or "conversational" is useless. You need tactical, granular instructions.

Instead of a generic deny list, give it a positive command. Try a prompt prefix like: "Write this as if you're a skeptical industry expert who hates marketing fluff. Replace any abstract business jargon with concrete, specific language about what the product actually does." Then paste your actual request.

But the real secret? Feed it your best existing copy. If you have a blog post or customer email that everyone loved for its plainspoken style, paste that whole thing into the "Examples" field. Show it, don't just tell it.

And yeah, it's everyone's problem. The tool is just mirroring the mediocre training data it got. The fix is more editorial work upfront, not less.

I'm just getting my head around this too. The advice about showing it your own good writing really helped me. I found an old, simple FAQ document our team wrote that everyone liked, and I pasted the whole thing into the "Examples" or "Reference Text" section. It made a noticeable difference.

I still struggle with the prompts though. You mentioned step-by-step advice - could you share what exactly you type before your main request? I've been using "write this for a real person, not a marketer" but I'm not sure it's strong enough.

Everyone misses the point. The tool is a mirror. Your marketing leads probably talk like that in meetings. The bot just reflects your own internal culture.

All the prompt tweaking and deny lists are temporary patches. If you want authentic content, you need someone to write it. The bot is optimized for volume, not quality. It's picking the most statistically common corporate phrases because that's what it was built to do.

You're trying to fix the symptom, not the cause. You'll burn hours on settings and still get generic output.

You're right to focus on prompt structure. The Brand Voice section is a blunt instrument - think of it as setting your budget guardrails, not writing the content.

Instead, put your detailed rules in the prompt itself. Start every request with a clear, negative instruction. For example: "Do not use any corporate buzzwords, cliches, or abstract jargon. Replace any such language with simple, direct statements about features and outcomes." Then give your actual request.

It's a common issue because these models are trained to predict the most probable word, and buzzwords are statistically probable in their training data. You have to actively steer it away from that default.

Exactly. It's like telling your AWS bill to stop being expensive - a direct 'stop' command is good, but you need to reroute the underlying traffic. A negative instruction works, but it can leave a vacuum.

Better to combine it with a positive, concrete example in the same prompt. After "Do not use jargon," immediately add: "For example, instead of 'leverage our synergy,' write 'our X feature works with Y to do Z.'"

It forces the model to replace, not just delete, which is how you avoid the next probable buzzword in line.

user1598 has it right. You're treating a statistical inevitability as a prompt engineering problem. The core training data is a swamp of corporate fluff, so the output is slop.

All those brand voice settings and deny lists are just filters on a polluted stream. You'll ban "synergy" and get "paradigm shift." Ban that, get "game-changer."

If you're stuck with the bot, paste the worst examples you get into the prompt and add: "Rewrite the above to sound like a human wrote it, not a press release. Be specific, not vague." It's a hack, but it works better than chasing keywords.

You're getting a lot of good tactical advice here, but I want to emphasize a workflow point. The problem often isn't just the initial prompt - it's the lack of a human editing layer.

Don't expect ContentBot to deliver a final draft. Use it for the raw material, then have a writer or editor pass over it with a clear mandate to strip out the jargon and make it sound human. Treat the output as a first pass, not a finished product.

For your prompts, try giving it a role and an audience it's afraid of. Something like "Explain this to a room of cynical, senior engineers who will call you out for using fluff." It changes the probability calculation.

Yes, the workflow fix is the real one. I treat my data pipelines the same way - the raw extract is never the finished product. It goes through transformation, quality checks, and validation layers.

Treating bot output as a first-pass source draft is a smart analogy. You wouldn't publish raw, unfiltered log data to a dashboard, so why publish raw LLM output? It needs a human transformation step.

The "audience it's afraid of" trick is clever. Makes me think of how I write SQL for a critical colleague versus for myself. The mental model of a skeptical reviewer changes the output before it's even written.

You're not wrong about the statistical probability of buzzwords, but the mirror point is critical. I've seen this in dashboard design, where everyone requests "actionable insights" but then complains the output is generic. The tool gives you the mean of your organization's language.

We treat content generation like a query to be optimized. The input (prompt) is weak, the underlying data (training corpus) is corporate, and we're surprised by the output. The real fix isn't a better WHERE clause, it's better source data. That means feeding it human copy, as others said, or accepting it's a cheap, mediocre first draft for a human to rewrite.

You're on the right track with using your team's FAQ as a reference text, that's a great data source for it to learn from. It's feeding the model better source material, which is half the battle.

For your specific prompt question, "write this for a real person, not a marketer" is a decent start, but it's a bit vague. The model needs more specific constraints. Try writing the prompt as if you're giving instructions to a junior copywriter who is prone to corporate speak.

Something like: "You are writing a plain-text email to a busy product manager who hates marketing fluff. Your goal is to be understood on the first read. Do not use any metaphors, analogies, or abstract terms. Use simple, direct sentences. Now, here's what I need you to write about: [your request]."

It provides a clearer role and a concrete audience, which often works better than a general "don't be a marketer" rule.

The brand voice section is mostly placebo. It's like trying to fix a leaky pipe by painting it.

You're asking for a step by step guide to make a stochastic parrot stop sounding like a parrot. The problem is statistical, not configurational. The model's training data is saturated with that corporate fluff because that's what fills the internet. Tweaking prompts is just applying a slightly different filter to the same noisy signal.

Instead of banning "synergy," feed it better examples. Take your best performing, most human-sounding blog post and paste the entire thing into the prompt as a reference. Then ask it to write in that style. You'll have more luck biasing it toward your one good example than trying to blacklist the infinite world of bad ones.