I built AmplifySignal to act as a blog autopilot for indie makers and small sites, and I run it on my own sites. When I set up a new automated blog, the hardest part isn't generating the text. It is stopping the generator from making up facts. A large language model has read millions of marketing pages. By default, it writes a generic sales pitch for a product that doesn't exist, inventing features, pricing tiers, and integrations along the way.
To stop this, I rely on a rigid fact sheet. When I define standing instructions: what belongs in a project site line determines exactly how accurate, specific, and useful the drafted articles will be. A site line is not marketing copy. It is not a prompt template meant to coax creativity out of a system. It is a dense, plain-text configuration of what a product is, who it serves, what it costs, and crucially, what it does not do.
The absolute facts
I start with the raw mechanics of the product and drop all adjectives. If the tool tracks server uptime, the site line shouldn't call it an ultimate monitoring solution. It should state it is a Node.js cron job that pings endpoints every sixty seconds and sends SMS alerts via Twilio.
I detail the exact pricing. I detail the exact hosting requirements. I detail the tech stack. When a target keyword is passed to the system, the drafted article pulls from this repository of facts to anchor its paragraphs. If a fact isn't in the site line, the generator is not allowed to mention it. This ensures every piece of content remains grounded in the actual shipped product.
Defining audience nouns
Indie makers build tools for specific niches. The standing instructions must define who the reader is and the nouns they use in their daily work. Consider the difference between an application aimed at enterprise managers versus one aimed at solo developers.
For the manager, I enforce words like reports, allocation, quarters, and direct reports. For the solo developer, the exact same core technology requires a completely different vocabulary: commits, deployments, issue trackers, and terminal commands.
Without these constraints, the model defaults to corporate abstractions like synergies and workflows. A paragraph about optimizing a workflow means nothing. A paragraph about automating invoice generation from merged pull requests proves the article understands the reader's actual day.
Negative constraints and missing features
What a project does not do is often more important than what it does. Large language models assume standard software parity. They have ingested thousands of pricing pages, and they expect certain enterprise features to exist.
If I do not explicitly forbid it, a drafted article will eventually claim the software offers priority support, compliance certifications, dedicated account managers, or team-based permission roles. A single hallucinated feature destroys the credibility of the entire post.
The site line must contain a brutal, honest list of absences. I write commands forbidding the mention of team plans, cloud sync, or mobile applications. These negative constraints act as a firewall. When the model attempts to generate a paragraph about how a tool scales for large teams, the standing instructions force it to pivot back to how the tool operates for a single user on a local machine.
Setting structural guardrails
Standing instructions also dictate the shape of the text. Most generated content is recognizable by its structure: long introductory fluff, a list of bullet points, and a conclusion summarizing the introduction. I fix this in the site line by forbidding those structures.
I add rules specifying short paragraphs and prohibiting bullet-point listicles unless the topic is explicitly a list. I ban introductory filler and specific recognizable phrases. If I want sentence-case headings, I state it. If I want links formatted as standard HTML tags on natural words, I state it. These structural guardrails force the model out of its default, highly recognizable patterns.
Handling tone without voice matching
A common trap when writing a site line is attempting to describe a voice. Instructions telling a model to write in a quirky tone yield terrible results. The model amplifies the instruction into a caricature, writing text that reads like an overly enthusiastic mascot.
Replicating a specific human voice from writing samples is a complex problem. I keep it on the roadmap, but I never claim the current system sounds like you. Instead, I manage tone through strict mechanical rules about perspective and phrasing.
I instruct the model to write in the first person. I tell it to favor concrete nouns over abstract concepts. I require it to state how a thing is done rather than offering generalized advice. A plain, specific explanation naturally sounds more human than any attempt to engineer a conversational tone.
Applying first-person perspective
First person carries opinions and method. When I instruct the model to write in the first person, I do not allow it to invent a biography or fabricate a daily routine. I tell it to describe how a task is completed using first-person pronouns.
Instead of saying developers should check their logs, the text says I check my logs. This subtle shift transforms a preachy tutorial into a shared technical method. It keeps the tone grounded in practical craft knowledge rather than theoretical advice.
Writing for the maintainer's peers
When a site line doesn't explicitly name a target user, I default to addressing my peers. For indie makers, that means writing for other people who build with the same tools. The site line should list the frameworks, databases, and deployment strategies used to build the product.
If a project uses Next.js and is hosted on Vercel, that goes in the fact sheet. When an article touches on technical subjects, it uses the correct terminology for that stack. It won't give generic advice about choosing a server; it will talk about edge functions and build times. This specificity separates useful articles from generic output and demonstrates actual craft knowledge.
Connecting the site line to automation
This configuration file is the core engine for drafting content. I take a target keyword—perhaps one picked by finding striking distance keywords in Google Search Console where the site already ranks at position eight through twenty—and pass it alongside the site line and standing instructions.
The system returns a drafted article written strictly against those facts and schedules it as a post in Ghost, WordPress, or on a hosted blog served from the domain. The site line ensures the draft requires minimal editing before it clears the optional review hold and publishes.
When I target a keyword where a site ranks just off the first page, the goal is to push that ranking to the top five. A generic article filled with marketing fluff will not rank higher. Search engines look for topical authority and specific terminology. By forcing the generator to use the precise nouns and technical constraints outlined in the site line, the resulting article naturally demonstrates expertise on that exact keyword.
Managing distribution constraints
The facts in the site line don't just fuel the blog posts; they inform the distribution pipeline. Because earlier articles cover how to automatically turn blog posts into social media posts, I do not repeat the specific network dialects in the core site line. Instead, the standing instructions manage the mechanical limits.
I specify which platforms receive automated publishing. A Bluesky post goes out about two hours after the article publishes, and a LinkedIn post goes out the next morning. X, Threads, and Mastodon remain manual tasks where the post is written and handed over as a prefilled composer link.
The standing instructions enforce the character limits for these prefilled links, set the weekly caps per channel, and dictate that failed posts retry with backoff. I enforce rules prohibiting emojis and limiting hashtags. When I click the prefilled link for X or Mastodon, the composer opens with a post that already adheres to my strict formatting rules, ready to send without further editing.
Treating the site line as code
A project site line is never finished. I run a keyword through the pipeline, read the resulting draft, and find a phrase I hate. The model might use a metaphor about a compass, or it might assume the software works offline.
When this happens, I do not just edit the draft. I edit the site line. I add a rule strictly forbidding navigation metaphors or always specifying the app requires an internet connection. Every mistake the model makes is a missing constraint in the standing instructions.
Over time, this document grows from a basic fact sheet into a highly specific compiler for the blog. It catches edge cases. It filters out bad habits and prevents regression.
The anatomy of a complete instruction set
A functioning site line usually breaks down into four distinct text blocks.
The first block defines the product facts. This includes the name, core function, tech stack, and deployment method.
The second block lists the audience and their nouns. It provides the strict vocabulary the model is allowed to use.
The third block handles the negative constraints. This lists everything the product lacks, everything the model is forbidden from saying, and the marketing filler it must avoid.
The fourth block details the formatting. This covers paragraph length, heading styles, link rules, and structural requirements.
A minimal standing instruction block looks like this:
Product: A macOS menu bar app for tracking time against GitHub issues. Stack: Swift, native UI, SQLite.
Audience: Independent developers and freelance programmers.
Nouns to use: Repos, commits, PRs, terminal, Xcode, billing cycles.
Negative constraints: No cloud sync. No team features. No Windows or Linux versions. No mobile app.
Tone: First person. Plain, direct, specific. No bullet-point listicles. Short paragraphs.
Banned phrases: Unlock your potential, delve, in today's fast-paced world.This rigid structure leaves no room for interpretation. The model cannot invent a Windows version, it cannot write a listicle about unlocking productivity, and it must address the reader using the vocabulary of a working developer.
When these blocks are combined, they form a strict framework. The language model ceases to be a creative writer guessing at intent and becomes a parser, formatting facts into readable paragraphs based on the target keyword. If you need a pipeline that respects these constraints, AmplifySignal applies your site line to target keywords to automatically draft, schedule, and distribute your blog posts.