Enhance Your Docs UX with AI: A Hands-On Workshop for Angular | Katerina Skroumpelou | ng-conf 2024

An AI assistant for documentation enhances discoverability by allowing users to describe what they’re looking for in natural language, rather than relying on exact keyword matches

Key components for building an AI docs assistant:

• Split documentation into manageable sections
• Generate embeddings (vector representations) for each section using OpenAI’s API
• Store embeddings in a vector database (like Supabase)
• Match user queries against stored embeddings to find relevant context
• Use GPT to generate natural language responses with retrieved context

Benefits of adding AI to documentation:

• Improved search and discovery
• Natural language understanding
• Ability to combine information from multiple doc sections
• Better user experience through conversational interface
• Helps identify gaps in documentation based on user queries

Implementation considerations:

• Monitor token usage and costs
• Implement streaming responses for better UX
• Add user feedback mechanisms
• Keep embeddings up-to-date with documentation changes
• Choose appropriate OpenAI models based on needs

Best practices:

• Use checksum verification to avoid regenerating unchanged content
• Implement proper error handling
• Set appropriate thresholds for embedding matches
• Consider rate limits and API costs
• Add clear sourcing to responses

Integration options:

• Can be self-hosted or cloud-based
• Works with existing documentation systems
• Can combine multiple documentation sources
• Supports automated updates through GitHub actions
• Can be enhanced with chat interfaces

Cost considerations:

• Embedding generation is relatively inexpensive
• Main costs come from GPT completions
• GPT-3.5 provides good results for most doc use cases
• Can optimize token usage to control costs