Case Study

AI Content Pipeline

Automated content generation system running inputs through LLM chains for drafting, editing, and formatting. Human-in-the-loop review and version tracking built in.

LangChainGPT-4RedisReactFastAPI

3-stage LLM chain

70% reduction in drafting time

Full version diff audit trail

The Problem.

Content teams spend too much time on repetitive drafting and formatting. They need AI assistance that maintains brand voice while keeping humans in control of quality.

Architecture.

3-stage LLM chain: research → draft → edit/format

Brand voice fine-tuning with few-shot examples per client

Redis queue for async content processing jobs

Human-in-the-loop review interface with inline edit suggestions

Version tracking with diff visualization between drafts

FastAPI webhooks for CMS integration

Technical Challenges.

Maintaining consistent brand voice across different content types — solved with dynamic few-shot example selection based on content category

Built diff-based version tracking that shows exactly what the AI changed between draft iterations

Designed the review interface to minimize friction — reviewers can accept/reject changes inline without context switching

Results.

3-stage pipeline produces publish-ready content with minimal human editing
Version tracking provides full audit trail of AI-generated changes
Human-in-the-loop review ensures quality while reducing manual effort

Interested in building something similar?

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