ChatWP

Answers WordPress-specific questions by retrieving and synthesizing information from official WordPress documentation using retrieval-augmented generation (RAG). The system indexes the complete wordpress.org documentation corpus, performs semantic search to identify relevant pages, and generates responses grounded in official sources rather than general LLM training data. This architecture minimizes hallucinations by constraining the answer space to documented APIs, functions, and best practices.

Provides a pre-built, embeddable chat widget that WordPress site owners can install on their websites to offer AI-powered support to visitors. The widget integrates via JavaScript snippet injection, maintains conversation state in browser-local storage or backend sessions, and routes queries to the ChatWP documentation-grounded inference engine. Styling and behavior are customizable through a dashboard configuration interface without requiring code modifications.

Retrieves and explains WordPress functions, hooks, and classes by matching user queries to the official WordPress code reference. The system performs semantic matching between natural language descriptions and function signatures, then returns the official documentation including parameters, return types, usage examples, and related functions. This enables developers to understand WordPress APIs without memorizing exact function names or navigating the reference site.

Maintains conversation history across multiple user messages, allowing follow-up questions that reference previous answers without requiring full context re-specification. The system stores conversation state (either client-side in browser storage or server-side in sessions), includes relevant prior messages in the context window sent to the inference engine, and uses conversation history to disambiguate pronouns and implicit references in subsequent queries.

Analyzes incoming user queries to determine whether they fall within WordPress documentation scope, and routes them appropriately to the documentation-grounded inference engine or provides a graceful out-of-scope response. The system uses intent classification to distinguish between WordPress-specific questions (e.g., 'How do I use wp_query?') and general programming questions (e.g., 'How do I write a Python script?'), preventing hallucinations from attempting to answer outside its domain.

Automatically tracks and displays the source documentation pages for each answer, providing users with links to official WordPress documentation and enabling verification of information. The retrieval system maintains metadata about which documentation pages contributed to each response, and the response formatter includes these citations in the output. This transparency allows users to dive deeper into official sources and builds trust through source attribution.

Filters documentation and API references based on the WordPress version specified by the user, ensuring that answers reflect the correct APIs and best practices for that version. The system maintains version-tagged documentation metadata and can exclude deprecated functions or APIs that were removed in newer versions, or highlight version-specific differences when relevant.

Generates WordPress code snippets (PHP, JavaScript, or configuration) based on user requests, grounded in official WordPress best practices and coding standards. The system synthesizes information from WordPress documentation about hooks, filters, and APIs to produce working code examples that follow WordPress conventions (e.g., proper escaping, sanitization, nonce verification). Generated code includes comments explaining WordPress-specific patterns and links to relevant documentation.