The pipeline is intentionally grounded in both real travel workflows and synthetic seller-intelligence scenarios.
Portfolio project · repository · production RAG
RAG Pipeline with Guardrails
Most RAG demos stop at retrieval quality. This project is about the next layer: making grounded answers traceable, adding safety checks around risky behavior, and turning those checks into explicit release decisions instead of after-the-fact commentary.
Best read for groundedness, safety, and release discipline treated as one system.
Quick Read
The missing layer in most RAG work is operational discipline.
Enough to prove the pipeline, guardrails, and reporting logic work together end to end.
The point is not the score alone. The point is having a score that can block or allow a release explicitly.
The problem
Retrieval quality alone does not make a feature shippable.
A grounded answer can still be risky, misleading, or hard to defend. The useful product question is whether the system can produce grounded answers, flag unsafe behavior, and explain why a release should move forward or stop.
System scope
- Dual-domain RAG pipeline with corpus-aware retrieval.
- Pre and post guardrails for prompt injection, PII, toxicity, and groundedness.
- Traceable outputs with citations, confidence, and verdict reasons.
- Benchmark reporting and PASS/WARN/BLOCK release logic.
Why this matters
This is the bridge between “the AI prototype looks promising” and “the team knows enough to launch responsibly.” That transition is where a lot of teams still get vague. I wanted the decision logic to be concrete.
Scope
- Travel corpus for SproutRoute use cases and a generalized seller workflow corpus for enterprise recommendation scenarios.
- Evaluation export in JSON and Markdown.
- Release gate tied directly to guardrail outcomes.
- Designed to fit with the evaluation and safety projects elsewhere in the portfolio.
Executive Summary
A good RAG system needs evidence, not just retrieval.
The answer has to be grounded, the risky behavior has to be caught, and the launch decision has to be legible. Once those three pieces live together, the system starts to look like production infrastructure instead of a retrieval demo.
Why it matters
- I think about RAG as a product system, not just a retrieval pipeline.
- I know how to combine safety checks and evaluation evidence into launch logic.
- I can connect technical quality back to product release decisions clearly.
Deep Dive
The interesting part is how the pieces fit together.
Dual-domain retrieval
The corpora cover both travel and seller workflows so the same pipeline can be tested against materially different user contexts.
Pre and post guardrails
Risky input and output behavior gets checked on both sides, which makes the pipeline safer and the evaluation more meaningful.
Evaluation harness
The benchmark suite is not there for decoration. It proves the pipeline and makes regressions comparable over time.
Release gate
Policy thresholds turn raw evaluation output into a decision artifact that can actually be used in launch reviews.