AI Supervision 10. The Blueprint for RAG Success: Integrating AI Supervision into Your Architecture
- TecAce Software
- 3 days ago
- 2 min read
"We built a RAG system, but where exactly does the evaluation tool fit in?"
"How do we map the retrieved documents to the actual answer for validation?"
The final puzzle piece in LLM service development is Architecture. It’s not just about calling an API; it’s about creating a seamless pipeline that Retrieves, Generates, and Evaluates.
In this final article of our series, we present a practical blueprint for integrating AI Supervision into your RAG (Retrieval-Augmented Generation) based service architecture.
1. Connecting the RAG Pipeline with Evaluation
The core flow of a RAG system is Query + Context (Documents) → Answer. AI Supervision sits as an observer within this flow.
Integration Point: It captures data right after the relevant documents are retrieved from the Vector DB and right after the LLM generates a response.
Key Role: Crucially, it captures the Context along with the answer. This is essential for calculating Faithfulness—determining if the AI actually used the source documents provided or just hallucinated an answer.
2. Architectural Diagram
Here is how AI Supervision integrates into your stack:
App Server: Receives user input and queries the Vector DB.
SDK/API Hook: The AI Supervision SDK acts as a middleware before and after the LLM call.
Pre-generation: Detects and blocks prompt injections or PII leaks.
Post-generation: Asynchronously sends the prompt, response, and context to the AI Supervision server.
AI Supervision Platform: Performs hallucination checks and accuracy scoring on the received data, visualizing everything on the dashboard.
3. Completing the Feedback Loop
The true value of this architecture lies in Continuous Improvement.
Ops → Dev: "Bad responses" detected in production can be added to the development team's TestSet with a single click.
Re-evaluate & Deploy: The dev team uses this data to refine prompts, runs regression tests, and deploys the improved model safely.
Conclusion: The Beginning of Trustworthy AI
We have now covered everything from Why you need supervision (Part 1), to Security (Part 2), Automation (Part 3), and finally Architecture Integration (Part 10).
The success of an AI service depends not on how smart the model is, but on how well you control and verify it. Complete your AI architecture today with AI Supervision.
Amazon Matketplace : AI Supervision Eval Studio
AI Supervision Eval Studio Documentation
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