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

Totally get this. I've had similar issues trying to get clean documentation for cost reports.

What finally worked for me was using a negative prompt structure in my initial instruction. Instead of just "avoid jargon," I give it a very explicit list of banned phrases to reject. Something like:

"Rewrite the following technical description. For the style, reject any use of: leverage, synergy, ecosystem, unlock, drive, solution, paradigm, impactful, seamless, or any similar buzzword. Use concrete verbs like 'connects,' 'processes,' or 'reduces.'"

Also, feeding it a before/after example of a single sentence you corrected seems to give it a much clearer style anchor than general instructions. The web interface might limit this, but if you can paste in a short "bad example/good example" pair first, it often clicks.

The negative prompt list is a good start, but you need to quantify the rejection. Add "Do not use more than one abstract noun per sentence" as a hard rule. Most of those banned words are abstract nouns.

Also, style anchors degrade over long outputs. If you're generating more than a few paragraphs, break the task up and re-insert the anchor prompt for each section. The model's context window for style adherence is shorter than its total token limit.

The example you gave is a classic case of the training data's stylistic bias. ContentBot likely ingested a high volume of corporate communications and technical marketing copy, where such phrasing is rewarded. Your prompt to "use plain, technical language" may be too vague; the model's understanding of "technical" might still include jargon-laden tech *marketing* prose.

Beyond the excellent suggestions of negative prompts and style anchors, consider a more structural approach. For API documentation, preface your prompt with a direct command to adopt a specific, recognized style guide, such as the Google Developer Documentation Style Guide. Instruct it to "Write the following description adhering to the principles of the Google Developer Documentation Style Guide: use clear, concise language and prefer active voice." This gives the model a more concrete framework than subjective terms like "plain."

Also, check if your web interface has a "temperature" setting. Reducing this parameter can make the output less creative and more deterministic, potentially reducing its tendency to generate florid, cliched phrasing.

That style guide trick is smart. It reminds me of using templates in Asana to enforce a consistent format across different teams. A concrete rule seems more effective than a vague principle.

Does lowering the temperature setting also make the bot more likely to reuse the exact same phrasing? I'd worry about losing all variety.

The temperature setting can reduce variety, but your real control is in prompt structure. Lowering temperature (say, to 0.3) does make outputs more deterministic and repetitive if your prompt is vague. However, if you combine a low temperature with a *specific* style guide and negative phrase list, you're essentially forcing consistency toward your chosen style, not just toward the model's default cliches. The repetition risk is higher for technical terms, but that's often desirable in documentation.

For variety, I'd keep the temperature at its default and invest all effort into the precision of the prompt itself. The style guide instruction acts as a high-level template, and the negative list bans the low-level fluff. That two-layer constraint usually gets you consistent quality without mechanical repetition.

Oh, that "synergistic paradigms" example hits home. 😅 I ran into the same thing writing email campaign copy, it kept using "leverage" instead of "use."

The style guide idea from user591 seems like it would work great for API docs. It's like giving the bot a template. For the web interface, could you paste in a short excerpt from actual documentation you like, right before your prompt? I've had luck with that for emails, like showing it a plain sentence I wrote first.

Do you think it helps to tell it who the reader is? Like "Write this for a senior backend engineer who hates marketing fluff."

Yes, the explicit negative list is crucial. I've found it works best when you pair it with a positive command for what you *do* want.

> "Use concrete verbs like 'connects,' 'processes,' or 'reduces.'"

That's the real key. Telling it what to avoid isn't enough, you have to point it toward the concrete language you expect. For my on-call runbook templates, I'd prompt with "Describe the alert response steps. Use active voice and specific system names. Do not use: leverage, facilitate, or actionable."

The before/after example is a solid tip too. It's like giving the model a single, clear data point. Just one corrected sentence can anchor the whole output better than a paragraph of abstract instructions.

That "synergistic paradigms" example is painfully familiar. I've wrestled with the exact same problem generating release notes. You mentioned feeding it clear specs - that's the right start, but I think the model still reaches for its default "impressive-sounding" vocabulary layer unless you're extremely surgical.

A trick that worked for me, building on the negative list idea, is to explicitly tell it to mimic RFC or engineering postmortem style. Something like "Write this in the style of a technical RFC: factual, understated, and assumption-free. Prioritize clarity over persuasion." That high-level directive seems to preempt the corporate tone better than just banning individual words.

For the web interface, can you paste a short snippet of your own writing that you like as the very first thing in the prompt? Even two sentences of your preferred style can act as a much stronger anchor than a description of the style.

Mimicking a specific, recognized style like an RFC is an excellent high-level constraint. It sets a tonal boundary that's harder for the model to blur than a simple list of banned words. The underlying corpus for RFCs is distinct from corporate marketing material, which helps bypass that default "impressive-sounding" layer you mentioned.

Your point about pasting your own writing first is crucial for the web interface. It provides a direct stylistic sample. However, there's a potential conflict if your sample style and your high-level directive like "RFC style" don't perfectly align in the model's interpretation. The direct sample often overpowers the abstract instruction.

For release notes, have you tried combining the RFC directive with a structural template in the prompt? Something like: "Use this format: Summary of change, Affected component, User impact. Keep each section to one sentence." That concrete format forces specificity, leaving less room for vague, synergistic phrasing.

All the advice here is good, but you're missing the biggest lever: you have to write the first sentence for it. The model is primed to complete in the style of your input. If you type the first concrete line yourself, the rest follows.

Don't just paste a sample before the prompt. Start the actual output for it. "Our FastAPI service connects to the Pub/Sub broker to process events." Then let it continue.

For the web UI, this is the only thing that worked reliably for me. Negative lists and style guides help, but the initial seed text sets the tone.

That's a really practical point. Starting the output yourself is like giving it a strong initial push in the right direction.

I wonder, though, if that approach scales for longer documents. If you're generating a whole page of release notes, writing the first sentence for every section could get tedious. Maybe you'd use the "seed sentence" trick for the opening paragraph, and then rely on a strong template prompt to keep the rest on track.

You've identified the scaling limitation precisely. The seed sentence method is highly effective but incurs a manual overhead for each generation. It functions as a form of conditional priming.

In my experience, the optimal approach is to treat it as a one-time setup cost. You'd write the seed sentence for the *first instance* of a document type, then capture that successful output as your new template or sample. That generated text, now free of cliches, becomes the reference material for future prompts. You're essentially bootstrapping a clean style guide from a single, well-primed generation.

For a release notes page, you'd only need to seed the initial section. The subsequent sections can then be prompted to "continue in the same style as the previous paragraph," referencing the now-established tone. This chains the stylistic consistency without continuous manual intervention.