How to Make a Model Follow Your Channel Rules Every Time

A rule is testable when you could hand it to a stranger with a word counter and get the same verdict as you. That means three components per rule: a unit, an operator, and a boundary that names what is included.

Compare these:

Two details do more work than people expect. First, state the counting convention, because word counts are ambiguous at the boundary. Second, give the substitute for a banned behavior. A model told never to use a word will often find a near synonym; a model told which word to use instead will comply.

System prompt design guidance converges on the same structure: role and audience, objective, constraints, output format, and handling for missing information, with critical constraints placed at the beginning where they carry the most weight (Coursiv, Promptessor). Notice that examples alone are listed as one building block, not the whole prompt.

A workable skeleton looks like this:

ROLE
You write derivative posts for the [channel] account of [brand].
You receive one source document and return one finished post.

HARD LIMITS
- Body: 180 to 220 words, counted by whitespace-separated tokens.
- Total output: 260 words maximum, including hashtags.
- Exactly 3 hashtags, lowercase, final line.

EXTRACTION RULES
- Preserve these facts if present: the primary statistic, who produced it, and the date of publication.
- Do not add facts that are absent from the source.
- If a required fact is missing, write "not stated" in the NOTES block.

VOICE
- Second person, present tense, no exclamation marks.
- Banned words: revolutionary, game-changing, unlock, leverage (verb).

OUTPUT
1. Hook line (maximum 12 words)
2. Body
3. Hashtags
4. NOTES block: word count, rules that nearly failed

The NOTES block matters more than it looks. Asking the model to emit its own word count turns a silent violation into a visible one, and a mismatch between its number and yours immediately tells you the rule is ambiguous rather than ignored.

Prose describes the rules; examples show where the edges are. Few-shot demonstrations condition a model toward the pattern you want, and research summarized by the prompting guide shows that the format of the demonstrations matters for performance, sometimes independently of whether the labels attached to them are even correct (Prompting Guide). Translation: the model is learning shape from your examples, so the shape has to be exact.

Two principles keep examples cheap. First, include one compliant and one non-compliant example per contested rule, not per rule. A prompt with twelve examples is slow, expensive and often contradictory. Second, annotate the failure. A bare "bad example" teaches nothing; a bad example with a one-line reason teaches the boundary.

EXAMPLE A (compliant, 204 words, 3 hashtags)
[full post text]
Why it passes: 204 body words, one parenthetical credit, URL in final paragraph.

EXAMPLE B (non-compliant, 291 words, 4 hashtags, exclamation mark)
[full post text]
Why it fails: 291 body words (71 over), four hashtags, one exclamation mark,
and the statistic appears with no attribution.

Repurposing is where these examples pay off, because a single source asset is expected to feed many smaller outputs, and the value comes from adapting ideas to each channel's conventions rather than pasting the same text everywhere (Hannon Hill, Multiview). Each channel variant gets its own example pair. That is the whole library: a few dozen short strings, maintained in version control alongside the rules.

The single highest-leverage line in the prompt is the one that asks the model to audit the draft against the rule list before returning it. Without it, the model generates once and stops. With it, the model gets a second pass in which the rules are the explicit task rather than background context.

The check should be mechanical, not reflective. "Review your answer for quality" produces nothing. "Count the body words and compare to 180 to 220" produces a number.

SELF-CHECK (run before returning)
1. Count body words. If outside 180 to 220, revise and recount.
2. Count hashtags. If not exactly 3, fix.
3. Search for each banned word. If found, replace with the listed substitute.
4. Confirm every preserved fact has a source location in the input.
5. Report the final counts and any rule you could not satisfy.

Two cautions. First, a self-check adds latency and tokens, so restrict it to rules that are actually being violated; a checklist of thirty items degrades into ceremony. Second, the model's self-reported counts can be wrong, which is why the final report includes per-rule status. When the model says "204 words" and a script says 231, you have found a counting-convention gap, and fixing that gap usually fixes a whole class of failures at once. Testing guidance for system prompts makes the same argument in stronger terms: treat the prompt as a testable software artifact rather than static text, because a poorly designed instruction layer produces inconsistent outputs, safety violations and degraded experience (testRigor).

Editing a prompt to fix one failure routinely breaks another. The only defense is a fixed test set you re-run after every change.

Five to eight inputs is enough to start, and they should be adversarial rather than representative:

• A source piece with no usable statistic (does it write "not stated" or invent one?).
• A source piece with a very long headline (does the hook rule hold?).
• A source containing a banned word in a quote (does it strip or keep?).
• A source that is far too long (does it extract or summarize?).
• A source in a different language (does it translate or refuse?).
• A source with two competing statistics (which one wins?).
• A source that is nearly empty (does it stop rather than pad?).

For each input, record a pass or fail per rule, not overall. A single quality score hides which rule broke. A table works well:

The passing set becomes your baseline. Any prompt edit that lowers the baseline gets reverted or scoped. This is ordinary software discipline applied to natural language, and it is what separates a prompt that works once from a system that works every time. The same rigor applies to the surrounding workflow, since content operations at scale depend on repeatable systems rather than one-off effort (Masset).

Suppose your newsletter promises a 200-word summary and your source piece runs 1,800 words. A prompt without units returns 280 words on average across five trial runs, 40 percent over.

Change three things and re-measure:

1. Replace "about 200 words" with "180 to 220 words, counted by whitespace tokens, excluding the URL."
2. Add one compliant example at 204 words and one non-compliant example at 291 words with the overage annotated.
3. Add a self-check that requires the model to print its own count.

Across the same five inputs, the failures typically move from the word budget to a new category, often extraction or hashtags, because the prompt now has a strong signal for length and weaker signals elsewhere. That is not a regression; it is a location. The next prompt edit fixes that rule and you re-run the five inputs to confirm nothing else moved.

Run the arithmetic explicitly. If a rule fails on 2 of 5 inputs before the fix and 0 of 5 after, you can state compliance as 60 percent rising to 100 percent on the test set, which is a claim you can defend. Without a fixed set, "it seems better" is all you have.

For teams running many channels, the same prompt plus a per-channel rule block and example pair is a single call. Routing text and other model types through one compatible endpoint keeps that pipeline simple; Quanta Inference puts text, image, video, audio and embedding models behind one OpenAI-compatible API key (Quanta Inference).

Prompts rot when edits are made in a chat window and forgotten. Treat the prompt as a document with version history, a changelog and owners.

Three habits carry most of the weight. First, one rule per line, with no compound sentences. "Be concise and cite sources" is two rules, and when it fails you will not know which half broke. Second, keep dynamic inputs out of the system prompt. The source piece, the channel name and the date belong in the user message, while the stable behavioral rules belong in the system layer, which is how providers structure instruction hierarchies in the first place (Promptessor). Third, when a violation appears in production, reproduce it as a new test input before editing the prompt, so the fix has a witness.

Also decide early what the model should do when rules conflict. A source that only supports 90 words under the extraction rule cannot also fill a 180 to 220 word budget. The prompt needs a stated precedence order, for example: extraction accuracy beats word budget, and word budget beats hashtag count. Providers of long-form content strategy make the same structural point about planning channels, resources and measurement before publishing, because undocumented decisions get relitigated every cycle (Capsule).

If the goal is to hand the whole routine to a specialist who reads your style guide, works the pipeline and returns finished drafts plus the test results, that is the kind of work Quanta Experts is built for (Quanta Experts).