Generating blog articles with RAG

Overview

• Designing an AI-assisted, step-by-step process and LLM pipeline for blog article creation

• Prompt engineering to improve how the AI understands and uses different writing styles

• Mapping out retrieval of additional data from reliable sources, data cleaning

• How to integrate retrieved information into the article generation pipeline for more robust content

• Designing the UI/UX and prompt engineering for an AI-assisted editor toolbar

LLM pipeline

1. User inputs and content outlining
• Guide user to provide topic or keywords
• Generate structured outline options using LLM for user to mix-and-match topic sentences

2. Search retrieval integration
• Query external data and trusted sources
• Incorporate key points from retrieved data as talking points
• Show citations and links to source material

3. Article generation
• Apply tone of voice and article length
• Combine user-provided data, generated data, and retrieved information to create a full-length article
• Naturally integrate target keywords

Key features

1️⃣

Drag and drop outline builder: Added a mix-and-match feature for multiple outlines while preserving generated history. This design choice came from the observation that writers often revise their outlines and dislike losing content they've already created.

• Allows combining different outline versions with drag-and-drop

• Capitalizes on loss aversion principle

• Speeds up outline creation process

 

1️⃣

Sub-points with RAG search integration: This feature enhanced content depth and reduced hallucinations by allowing user-added dot points for each outline section, with a programmatic “Research" button to integrate real-time internet searches to quarterback. I wanted to balance automated research with human creativity and encourage writers to be more hands-on and create value where it mattered—not just create a content mill.

• Allows writers to add and edit points to ensure their own personal perspective

• Integrates real-time internet searches for additional context

• Streamlines research process within the platform and prevent context switching

 

1️⃣

Source integration: I designed a source section with links to sources directly within each section, inspired by bibliography formats. This design choice serves two purposes: it builds authority and helps users trust the information by allowing them to verify it themselves.

• Intentionally prominent display for user awareness

• Builds content authority

• Allows users to verify information easily

Displaying so much information in one section can overwhelming—future iterations might include options to collapse sources or make them less conspicuous to better balance usability with transparency.

1️⃣

 

Style, tone, and inputs control panel: A customizable interface gives writers precise control over their content's voice and structure. This allows users to fine-tune their writing and make sure it aligns with their intended style and audience expectations.

• Fine-grained inputs for adjusting writing style

• Designed to accommodate various writing workflows

• Allows writers to tailor their content for specific audiences or platforms

Since it’s a secondary feature with many options, I wanted to keep it clutter-free while keeping it discoverable. I highlighted the panel in the onboarding tutorial sequence to improve discovery instead.

 

Takeaways

Future improvements

• Implement SEO-focused features, such as keyword density analysis and suggestions

• Add multi-modal content creation and retrieval for rich media integration

• Create collaborative features for team-based article creation and editing