RAG Vendor Evaluation and Swap Protocol (Onboarding)

RAG Vendor Evaluation and Swap Protocol (Onboarding)

1. 01

   Define retrieval quality benchmarks before comparing vendors

   Set measurable criteria such as answer accuracy, citation fidelity, and latency targets. For example, a client-facing chatbot might require 95% source-grounded answers and sub-2-second retrieval.

2. 02

   Inventory the client's document corpus and access patterns

   Catalog file formats, volume, update frequency, and permission structures. This determines whether a managed context engine API like Ragie, which handles multimodal ingestion, is necessary or if a simpler vector store suffices.

3. 03

   Run a blind accuracy test across candidate tools

   Use a fixed set of 50 representative queries and have two reviewers score responses for relevance and source grounding. Include edge cases like ambiguous queries and documents with conflicting information.

4. 04

   Assess total cost of ownership over a 12-month horizon

   Compare per-request pricing, storage costs, and any overage fees. Factor in the recent trend of model price drops, such as the 80% cut on GPT-5.6 Luna, which can shift the economics of retrieval-heavy workflows.

5. 05

   Verify security and compliance certifications

   Confirm SOC 2, GDPR, and any industry-specific standards. Given the rise of AI-related security incidents, such as the Hugging Face credential breach, ensure the vendor's data handling meets client requirements.

6. 06

   Prototype a minimal integration with your existing stack

   Build a proof of concept that connects the RAG tool to your orchestration layer. Measure integration effort, error rates, and developer experience. This validates the vendor's API quality and documentation.

7. 07

   Document the evaluation results and decision rationale

   Create a comparison matrix with scores for accuracy, cost, security, and integration ease. Store this in a shared knowledge base so future swaps are informed by past learnings.