RAG Implementation

Automated Tagging

Prepare inputs and settings through Automated Tagging before running rag implementation.

Why this matters

Automated Tagging sets up the foundation for rag implementation; clean inputs here reduce downstream rework.

What you get

Inputs, context, and settings are ready so the workflow can move into execution without blockers.

Drupal AI →

Drupal AI handles automated tagging with precision — The open-source enterprise standard for decoupled AI orchestration and automated content workflows. Getting this preparation step right avoids rework later in the rag implementation pipeline.

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ViSenze

Freemium

2Input · Step 2 of 6

DXP Integration

Use DXP Integration to build supporting assets that improve rag implementation quality.

Why this matters

DXP Integration strengthens rag implementation by feeding better supporting material into the pipeline.

What you get

Supporting assets from dxp integration are prepared and connected to the main workflow.

Drupal AI →

Drupal AI strengthens the rag implementation workflow by handling dxp integration — The open-source enterprise standard for decoupled AI orchestration and automated content workflows. Better supporting inputs here directly improve the final output quality.

3Core · Step 3 of 6

RAG Implementation

Execute rag implementation with RAG Implementation to produce the primary final deliverable.

Why this matters

This is the core step where rag implementation actually happens, so it determines baseline quality for everything after it.

What you get

A first-pass final deliverable is generated and ready for refinement in the next steps.

Drupal AI →

Drupal AI leads at rag implementation — The open-source enterprise standard for decoupled AI orchestration and automated content workflows. It consistently ranks as the highest-fit tool for this core step.

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Flowise AI

Freemium

4Refine · Step 4 of 6

LLM Abstraction

Refine and validate rag implementation output using LLM Abstraction before final delivery.

Why this matters

LLM Abstraction adds quality control so issues are caught before the workflow is finalized.

What you get

The final deliverable is improved, validated, and prepared for final delivery.

Drupal AI →

Drupal AI refines the workflow via llm abstraction — The open-source enterprise standard for decoupled AI orchestration and automated content workflows. Adding this quality step before final delivery prevents issues from reaching end users.

5Refine · Step 5 of 6

RAG Pipeline Construction

Refine and validate rag implementation output using RAG Pipeline Construction before final delivery.

Why this matters

RAG Pipeline Construction adds quality control so issues are caught before the workflow is finalized.

What you get

The final deliverable is improved, validated, and prepared for final delivery.

Dify.ai →

Dify.ai refines the workflow via rag pipeline construction — The Open-Source Standard for Building and Orchestrating Production-Ready LLM Applications. Adding this quality step before final delivery prevents issues from reaching end users.

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Langflow

Freemium

6Deliver · Step 6 of 6

RAG Pipeline Management

Package and ship the output through RAG Pipeline Management so rag implementation reaches end users.

Why this matters

RAG Pipeline Management is what turns intermediate output into a usable, publishable result for real users.

What you get

A finalized final deliverable is ready for publishing, handoff, or integration.

n8n →

n8n takes care of rag pipeline management — The fair-code workflow automation platform with a native AI orchestration layer. This is the final step that gets the rag implementation result in front of real users.