Tutorial: Build a Blog Post Generator Prompt You Can Actually Trust

What you will build

By the end of this tutorial you will have a reusable blog post generator: paste in a topic, get a structured first draft that follows your tone, hits the length you asked for, and never includes the filler phrases that mark AI-generated content from a kilometre away.

It is not magic. It is a prompt assembled deliberately, tested against five real topics, and corrected for the failure modes you actually see — which is the only way prompts become trustworthy.

This takes about 45 minutes the first time. After that, running it on a new topic takes 30 seconds.

Step 1: Decide what "good" looks like

Before writing any prompt, write a short description of the output you want. Three sentences is enough:

> A 600–800 word draft blog post. Structured as: hook (1 paragraph), three sections with H2 headings, a closing paragraph. Voice is plain and direct — no "in today's fast-paced world," no rhetorical questions, no AI-style transitions like "moreover" or "furthermore." Written for a knowledgeable reader who does not need basic concepts explained.

This becomes your evaluation rubric later. Without it, "is this output good?" has no answer.

Step 2: Write the first version of the prompt

Open a fresh chat session. Write a prompt that contains all five components of a strong prompt — role, task, context, format, examples.

You are an editor writing for a publication that values plain,
direct prose. Your readers are practitioners — they know their
field and have no patience for filler.

Task: Write a 600–800 word blog post on the topic provided.

Structure:
- Opening paragraph that states the point of the post directly.
  No rhetorical questions, no scene-setting.
- Three sections, each with an H2 heading.
- Closing paragraph with one concrete takeaway.

Voice rules:
- No "in today's fast-paced world" or similar openings.
- No transitional words like "moreover," "furthermore," "in conclusion."
- No bullet lists in the body — use prose.
- No headings that are questions.

Output format: Markdown. Begin with the H1 title on the first line.

Topic: [PASTE HERE]

Notice how much of the prompt is negative instructions. That is because the failure modes of AI blog generation are predictable and worth pre-empting.

Step 3: Run it on five real topics

Pick five topics from your actual content backlog. Do not invent topics for testing — invented topics produce invented results.

For each topic, run the prompt and save the output. Do not edit yet. You are collecting data.

Suggested test topics:

1. Something you know deeply.
2. Something you barely know.
3. A topic you have written about before.
4. A topic with a strong opinion baked into it.
5. A boring "how to" topic with no obvious angle.

Five runs is the minimum to see patterns. Three runs is too few — you will misdiagnose one-off failures as systemic.

Step 4: Identify failure modes

Read all five outputs in one sitting. Look for repeated problems, not single instances. Common findings in this kind of test:

• The H1 title is generic ("Understanding X") even when the post itself has a sharper take.
• The model sneaks in a bullet list despite the rule.
• The closing paragraph is filler.
• Section three is always weaker than sections one and two.
• "AI tells" — phrases like "it's worth noting that," "at its core," "ultimately."

Write each failure mode down. You will fix each one in the next step.

Step 5: Patch the prompt one failure at a time

Add a targeted line to the prompt for each failure mode. Do not rewrite the whole prompt — adjust surgically.

For the failures above, additions might be:

- The H1 must contain a specific claim, not a topic label.
  Bad: "Understanding API Design." Good: "Three API Decisions
  You Will Regret Within a Year."

- No bullet lists anywhere in the body. Use prose paragraphs.

- The closing paragraph must contain a sentence the reader could
  act on within the next 24 hours. No "in conclusion."

- Section three must introduce a counter-argument or an exception
  to the main thesis. Avoid summarising what sections one and two
  already said.

- Do not use the phrases "it's worth noting," "at its core,"
  "ultimately," "in essence," "at the end of the day."

Now run the same five topics again. The improvements should be obvious. If they are not, the patch was wrong — adjust again.

Step 6: Add a self-critique step

For higher quality, add a second pass. After the first draft, ask the model:

Now review the draft you just wrote. Identify three specific
weaknesses — one in structure, one in language, one in argument.
Then rewrite the draft addressing those weaknesses.

Output only the revised draft.

This roughly doubles the token cost and meaningfully improves the output, especially on topics where the model's first instinct is generic. For high-volume use you may skip this step; for posts that will actually be published it is worth it.

Step 7: Save it as a reusable template

Once the prompt is stable across five topics, save it. Give it a clear name, write a one-line purpose statement, document the known failure modes that you did not fully fix, and put it where you can find it.

# blog-post-generator-direct-voice

Purpose: Produces a 600–800 word blog draft in a plain, direct
voice for practitioner readers.

Inputs: A topic line.

Outputs: Markdown blog post starting with H1, three H2 sections,
no bullet lists.

Known failure modes:
- On topics with strong existing opinions online, the model
  sometimes adopts an unattributed second-hand view. Fact-check.
- Section three counter-arguments are occasionally weak. If so,
  prompt: "Strengthen the counter-argument in section three with
  a specific scenario."

What you have actually learned

The prompt is the deliverable. The skill you built is the process: write deliberately, test on real inputs, patch failure modes specifically, and ship a documented version. Every reusable prompt in your library should go through this same path. Most will take less than 45 minutes once you have done it once. The few that earn the time pay it back many times over.