Aimogen exposes a controlled set of WordPress hooks and filters that allow developers to intercept, extend, or alter AI execution without modifying core plugin files. These hooks exist for integration, customization, and automation, and they follow standard WordPress conventions so they behave predictably in real-world environments.
Hooks are intentionally placed at execution boundaries, not deep inside provider logic. This keeps customizations stable across updates.
Design Philosophy #
Aimogen hooks are designed to let you influence what happens and when, not to reimplement the AI engine. You are expected to adjust inputs, outputs, permissions, limits, and execution flow, not internal provider calls.
If something can be done via settings or workflows, hooks usually exist only to extend that behavior, not to replace it.
Execution Lifecycle Hooks #
During any AI execution, Aimogen passes through a predictable lifecycle. Hooks exist at key moments in that lifecycle so you can intervene safely.
Before execution starts, a pre-execution hook allows inspection and modification of the execution context. This is where developers typically alter prompts, inject additional instructions, modify metadata targets, or block execution conditionally.
During execution, hooks allow you to observe provider selection, model resolution, and fallback decisions. These hooks are observational by default but can be used to override behavior if needed.
After execution completes, post-execution hooks expose the final result. This is the safest place to modify generated content, attach metadata, trigger external systems, or log custom analytics.
Content Generation Hooks #
For AI content creation, Aimogen exposes hooks before content is sent to the AI and after content is generated.
Pre-generation hooks allow you to modify titles, prompts, structured inputs, target post types, or generation rules. This is commonly used to inject site-specific instructions or enforce editorial constraints programmatically.
Post-generation hooks receive the generated content before it is saved. This allows you to sanitize output, apply transformations, attach custom fields, or conditionally change post status. Because these hooks fire before persistence, they are safe and reversible.
AI Content Editing Hooks #
AI Content Editing has its own hooks because editing is destructive by nature.
Before editing, hooks allow you to validate whether content should be edited at all, modify the editing instruction, or abort execution based on context. This is often used to protect specific post types or content areas.
After editing, hooks expose both the original content and the edited output. This allows comparison, auditing, or conditional rejection of changes before they are committed.
Chatbot Hooks #
Chatbot hooks exist around message handling, not around UI rendering.
Before a chatbot message is processed, a hook allows inspection of the user input, conversation state, persona configuration, and embedding context. This can be used to block messages, inject system prompts, or route messages dynamically.
After a chatbot response is generated, hooks allow modification of the response, logging, lead capture logic, or triggering external actions. These hooks fire before the message is delivered to the user.
Conversation lifecycle hooks also exist for conversation start, termination, and timeout events, enabling advanced conversational control.
Embeddings Hooks #
Embeddings-related hooks fire during embedding creation and retrieval.
When embeddings are generated, hooks allow you to modify source content, chunking behavior, or metadata associated with vectors. This is useful when integrating non-standard content sources.
During retrieval, hooks allow inspection of retrieved chunks and relevance scores. Developers commonly use this to filter low-quality matches or enforce domain-specific constraints.
Provider & Model Hooks #
Provider resolution hooks allow you to influence which provider or model is selected for a given execution. These hooks fire after configuration is loaded but before execution begins.
They are commonly used to implement custom routing logic, such as selecting different providers based on language, content type, user role, or cost thresholds.
Fallback-related hooks expose why a provider failed and which fallback is being considered next. This makes advanced failover strategies possible without hacking internal logic.
Usage Limits & Permission Hooks #
Before any AI execution is allowed, Aimogen evaluates permissions and limits. Hooks exist at this stage to extend or override that logic.
These hooks allow you to enforce custom business rules, such as blocking AI usage outside business hours, applying external quotas, or integrating with membership or billing systems.
Because these hooks fire before execution, they prevent cost and execution side effects.
REST API Hooks #
REST API execution passes through the same hook system as UI-based execution.
Hooks allow inspection of incoming REST requests, validation of payloads, modification of execution context, and post-response handling. This makes it possible to integrate Aimogen cleanly into headless or external systems while preserving internal controls.
There are no “REST-only” shortcuts. API calls are treated as first-class execution events.
Logging & Observability Hooks #
Hooks are available when usage logs and execution logs are written. These allow you to attach additional metadata, forward logs to external systems, or correlate AI execution with your own application events.
These hooks do not allow suppression of logs. Observability is enforced by design.
Safety and Update Stability #
All hooks are additive. None require overriding core classes or replacing services. This ensures that customizations survive plugin updates and do not break internal assumptions.
Hooks are versioned implicitly. When a hook changes behavior or signature, it is treated as a breaking change and documented accordingly.
Direct modification of Aimogen core files is never recommended. Hooks and filters exist specifically to avoid that.
Best Practices #
Use hooks sparingly and deliberately. Modify inputs before execution, validate outputs after execution, and avoid deep assumptions about provider internals. Always test hook-based logic in staging, especially for bulk operations and chatbots. Treat hooks as integration points, not as internal APIs.
Summary #
Aimogen’s hooks and filters provide safe, update-resistant extension points across content generation, editing, chatbots, embeddings, providers, limits, REST API execution, and logging. They allow developers to customize behavior, enforce rules, and integrate external systems without touching core plugin code. When used correctly, hooks turn Aimogen into a flexible AI platform that adapts to complex workflows while remaining stable across updates.
