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How to Use AI to Generate Articles, Summaries, and Reports Automatically

Use AI workers to automate content generation pipelines — from URL summarization to full article drafts — without managing models or prompts.

S
Seek API Team
·

AI content generation has moved from novelty to infrastructure. Teams are using it for first drafts, summarization, product descriptions, meeting notes, SEO pages, and data reports — at volumes that would have required a large writing team two years ago.

The bottleneck isn’t the AI model. It’s building and maintaining the pipeline around it.

What AI workers on Seek API do

An AI worker on Seek API is a pre-built, production-ready function that wraps an AI model with:

  • A defined input schema (what you pass in)
  • Managed prompt engineering (the hard part, already done)
  • Model selection and fallback logic
  • Structured output (not freeform text — typed JSON)
  • Credit-based pricing per run

You don’t configure a model, manage API keys, write prompts, or handle rate limits. You describe the task in parameters; the worker handles everything else.

Article generation from a keyword

The ai-article-generator worker takes a target keyword and returns a structured SEO-optimized article:

curl -X POST https://api.seek-api.com/v1/workers/ai-article-generator/jobs \
  -H "X-Api-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "keyword": "best CRM software for small business",
    "wordCount": 1500,
    "tone": "informative",
    "targetAudience": "small business owners"
  }'

Response:

{
  "title": "Best CRM Software for Small Business in 2026: 8 Tools Compared",
  "metaDescription": "...",
  "outline": ["Introduction", "What to look for", "Top 8 tools", "Comparison table", "Conclusion"],
  "body": "...",
  "wordCount": 1487,
  "readingTime": "6 min"
}

The body is clean Markdown, ready to paste into your CMS or post via a blog API.

URL summarization

The gpt-page-summarizer worker fetches any URL and returns a structured summary:

{
  "url": "https://techcrunch.com/...",
  "title": "OpenAI raises $6.6B at $157B valuation",
  "summary": "OpenAI has closed a funding round worth $6.6 billion...",
  "keyPoints": [
    "Valuation of $157 billion, up from $86 billion in 2024",
    "Investors include Microsoft, Thrive Capital, and SoftBank",
    "Funds will be used to expand compute infrastructure"
  ],
  "sentiment": "positive",
  "readingTime": "4 min"
}

Building a content pipeline

Here’s how a media monitoring pipeline might use these workers:

// Every morning: summarize 20 RSS items and email the digest
async function morningDigest(rssItems) {
  // Step 1: summarize all articles in parallel
  const summaryJobs = await Promise.all(
    rssItems.map((item) =>
      submitJob('gpt-page-summarizer', { url: item.link })
    )
  );

  // Step 2: wait for all summaries
  const summaries = await Promise.all(
    summaryJobs.map(({ job_uuid }) => waitForJob(job_uuid))
  );

  // Step 3: compile and send digest
  const digest = summaries.map((s) => ({
    title: s.title,
    summary: s.summary,
    keyPoints: s.keyPoints,
  }));

  await sendDigestEmail(digest);
}

The entire pipeline — 20 articles summarized and compiled — runs in under 30 seconds.

Product description generation at scale

For e-commerce teams:

{
  "productName": "Ergonomic Mesh Office Chair",
  "category": "Office Furniture",
  "attributes": {
    "material": "breathable mesh",
    "adjustableArmrests": true,
    "maxWeight": "150kg",
    "colors": ["black", "grey"]
  },
  "tone": "professional",
  "length": "short"
}

Returns ready-to-publish description, bullet points, and meta description. For a catalog of 5,000 products, that’s 5,000 jobs running in parallel — done in minutes, not weeks.

Pricing

AI generation workers cost between $0.007 and $0.025 per run depending on output length and model used. A 1,500-word article runs at $0.018 — roughly what a single GPT-4 API call costs, but with prompts already engineered, formatting handled, and output structured.

For teams publishing 50 articles/month, that’s $0.90 in AI generation costs. The bottleneck is no longer the money — it’s having something worth saying.

What to use AI workers for (and what to avoid)

Good uses:

  • First drafts that humans edit and approve
  • Summarizing long documents
  • Product and category descriptions with factual attributes
  • Data report narration (“sales were up 12% vs. last month, driven by…”)
  • FAQ content based on support tickets

Bad uses:

  • Publishing AI output directly without human review
  • Anything requiring expert professional judgment (legal, medical, financial advice)
  • Content where factual accuracy is critical and can’t be verified

The workers are tools. Like any tool, the quality of the output depends on how thoughtfully you use them.

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