Draft staging rules that stop hallucinated product features require a strict boundary prompt tied to a static fact sheet. You must instruct the generation system to use only the explicit claims provided in the site line. The prompt must explicitly forbid inventing past articles, user counts, latency numbers, or integrations. If a feature is not named in the provided text, the rule must dictate that the draft cannot claim the product does it.
Why do generation models invent software features?
Language models predict the next plausible token based on their training data. If you feed a system a prompt about a billing application, the model draws on texts about existing billing software. Most billing software handles multi-currency transactions. The model predicts that your application probably handles them too. It inserts a paragraph praising your smooth multi-currency support.
Your application actually only processes US dollars. The model did not lie maliciously. It completed a pattern based on statistical likelihood. This happens because the model lacks context about your specific codebase. Without strict boundaries, it fills the void with industry averages. It assumes that if you built a scheduling tool, you also built calendar syncing.
The problem compounds when the model tries to sound authoritative. It will invent specific latency numbers to make a paragraph sound more convincing. It will generate a story about how you rebuilt the database to handle scale. None of this happened, but it reads perfectly well. You have to actively break this pattern-matching behavior.
How do you write a strict boundary prompt?
You stop pattern completion by breaking the assumption of standard features with explicit negative constraints. The system prompt must contain a section dedicated exclusively to boundaries. I use a directive that says to never invent the product's facts. You have to list the specific categories of things models like to invent. List user counts, revenue, latency numbers, build stories, and integrations.
You then provide a plain text fact sheet. This is your site line. The rule must state that anything claimed about the product must come directly from this site line. If it is not there, the system cannot claim it. This turns the language model from an improviser into a strict summarizer of your provided facts.
The placement of these rules matters. I put the negative constraints at the very end of the system prompt. Models pay more attention to the instructions at the end of their context window. If you put the boundary rules at the top, followed by two pages of formatting instructions, the model often forgets the boundaries and invents a Slack integration anyway.
What belongs in the product fact sheet?
The fact sheet is a dense, factual paragraph listing exactly what the software does, what platforms it runs on, and what manual steps the user takes. It is not marketing copy. It does not include adjectives. If your tool converts PDFs, the fact sheet says it converts PDFs to text files. It does not say it intelligently extracts data.
Keep the fact sheet isolated from the writing instructions. The site line is a variable that changes as your software changes. The writing instructions are static. By separating them, you ensure that the model always knows exactly where to look for factual claims.
It does not include future plans unless explicitly marked as a roadmap. If you tell the system that a feature is planned, the model will often write about it as if it is already shipped. You have to write a rule that says to never describe roadmap features as active. Better yet, leave unreleased features out of the staging data entirely until the code actually deploys.
Why is an optional review hold necessary?
Prompts fail when a model interprets a vague sentence in your fact sheet as permission to invent an entire sub-feature. You need an optional review hold before anything publishes. A review hold puts the draft into a staging status. You read it. You check it against reality.
If the model hallucinated a webhook payload, you delete that paragraph. You publish the corrected draft. I wrote about prefilled composer handoffs for manual social channels like X and Mastodon. The same principle applies to the main blog post. You have to inspect the output before it hits your production domain.
I built AmplifySignal to run on my own sites with this exact workflow. A keyword picked from Google Search Console data where it already ranks at position 8 to 20 becomes a drafted article written against a strict site line. The system stages the post in Ghost or WordPress and waits for approval. If a hallucination slips through the staging rules, the review hold catches it before publication.
How do word counts cause feature hallucinations?
When you tell the system to write a long article based on a short fact sheet, the model runs out of real facts and invents new ones to reach the target length. You might ask for 1500 words based on a 50-word site line. The model covers the facts in 300 words. To fill the remaining space, it invents case studies and fabricates customer quotes.
You fix this by relaxing the word count constraint or by providing more context. Write rules that allow the model to discuss the broader problem the software solves, rather than just the software itself. If the article is about database indexing, the model can write 1000 words about indexing theory without inventing features for your specific database tool.
The staging rules must direct the model's focus outward to craft knowledge, not inward to fabricated product specs. The reader wants to know how to solve their problem. They do not need a fictional feature tour. Keep the focus on the tasks, the files, and the apps the reader actually uses.
How do you test a draft staging rule?
You test boundaries by asking the system to write about a topic adjacent to your product, but outside its actual capabilities. If you have an email marketing tool that does not do SMS, prompt the system with a keyword about multi-channel marketing.
Read the resulting draft. If the draft claims your tool sends text messages, your boundary rule is too weak. You need to add a negative constraint stating that standard industry features do not apply. You iterate on the system prompt until the model refuses to invent the SMS feature and instead focuses only on the email capabilities listed in the fact sheet.
You should also test with highly specific keywords. I detailed this process in a post about high-impression low-click search queries. When a query is highly specific, the model tries to answer it directly. If your product does not solve that specific query, the model will invent a feature that does. The staging rule must force the model to answer the query using general knowledge, mentioning the product only where it genuinely applies.
Does temperature control stop hallucinations?
Temperature controls the randomness of token selection, but it cannot replace hard boundary rules. A low temperature produces predictable, safe text. A high temperature produces more creative, varied text.
If you set the temperature too high, the model becomes prone to hallucination. It will ignore your strict boundary rules in favor of generating a more interesting sentence. It will invent a past project or a fabricated integration just to make the paragraph flow better.
If you set the temperature too low, the text becomes repetitive. You have to find a balance. I keep the temperature relatively low for the factual extraction phase and slightly higher for the prose generation phase. The staging rules must act as a hard constraint regardless of the temperature setting. If the rule says no invented latency numbers, that rule must hold even at maximum randomness.
Why are negative constraints better than positive instructions?
A negative constraint tells the system exactly what to avoid, leaving no room for creative interpretation. A rule stating to never use an exclamation mark is a negative constraint. These are much more effective than vague positive instructions like telling the model to be factual.
When staging a draft, you compile a list of negative constraints based on past failures. If the model previously invented a specific user metric, you add a rule forbidding user counts to the rule set. Over time, this list becomes the core of your draft staging rules.
This forms a hard shell around your fact sheet. The model learns exactly where the fences are. It stops trying to guess what your software does and starts relying entirely on the provided text. You stop fighting the model and start directing it.
When do you update the provided fact sheet?
You update the site line only when you actually ship a feature to the production environment. The text provided to the generation system must always reflect current reality. If you deploy a new API endpoint on Tuesday, you add the API documentation to the fact sheet on Tuesday.
This keeps the generated drafts accurate. The staging rules remain static, but the facts they govern change. The model reads the new fact sheet, obeys the strict boundary rules, and writes accurately about the new API without assuming it also has webhooks.
If you change the site line before the code deploys, the system will write drafts about features that do not exist yet. This confuses users and breaks trust. The staging rules stop hallucinated product features by enforcing strict adherence to a single, easily updated source of truth.