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Alfa’s AI Article Pipeline

Alfa runs article generation as a multi-stage pipeline instead of a single monolithic prompt. This makes the system easier to recover, inspect, and improve over time.

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The Eight Stages

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Why the Pipeline Is Structured This Way

The pipeline model gives Alfa a few advantages over a one-shot generator:

• clearer progress tracking
• recoverable failed states
• better separation between research, writing, and polish
• easier export of structured article payloads

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Inputs That Matter

The article pipeline works best when three inputs are solid:

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Brand Context

The saved brand profile influences voice, product framing, internal links, and audience assumptions.

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Article Intent

Alfa normalizes freeform requests into a more explicit keyword and content shape, which helps the pipeline treat a request as a real article job rather than a vague prompt.

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Target Length and Format

The writing flow uses target word count and article category to shape the output. In the current UI, this includes formats such as listicles, topic guides, comparisons, alternatives guides, how-to guides, and reviews.

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Progress, Recovery, and Retry

Alfa persists article runs and supports status tracking rather than relying on a single request lifecycle.

That enables:

• active run monitoring
• stale run detection
• retry for failed runs
• reopening completed runs later

This recovery model is present in both the app and the CLI.

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What You Get at the End

A completed run can include:

• final article content
• editor blocks
• title
• word count
• metadata such as slug and meta description
• citations
• verification score when available
• hero or section image data when present

From there you can continue in the review workspace, export to files, or publish to WordPress.