Enterprise RAG Platform

Designing reliable retrieval-augmented generation systems for real-world use.

Focus: Production RAGScope: End-to-end systemStack: Python, LangChain, Pinecone, FastAPIGitHub →

Why I built this

I built this to explore patterns for making RAG systems reliable in production. Most RAG implementations I saw failed silently, and I wanted to understand how to prevent that.

What I learned

The biggest insight was that evaluation gates matter more than model size. A smaller model with proper evaluation beats a larger model without it. Also, retrieval boundaries are crucial. The system needs to know when it doesn't have enough information.

TL;DR

Built a production-grade RAG system with domain routing, per-domain indexes, and evaluation gates that prevent silent failures. Improved traceability and reduced unsupported answers through systematic evaluation.

Why this matters

Applying LLMs to enterprise knowledge bases breaks in predictable ways. Naïve RAG implementations fail silently in production, producing confident but incorrect answers. This project was about preventing that.

Problem

When naïve RAG is used, models hallucinate answers outside their knowledge boundaries, fail to route queries to appropriate domains, and lack mechanisms to detect and prevent failures before they reach users.

Constraints

Latency requirements: sub-second response times required. Incomplete or noisy documents: enterprise knowledge bases contain inconsistent formatting and missing metadata. Evaluation without human labels: measuring faithfulness and correctness at scale without manual annotation.

System design

The system uses an intent-based routing layer to direct queries to the right domain. Each domain has its own vector index. Retrieval combines hybrid search with reranking. Schema validation and abstain criteria act as contracts. An evaluation gate checks faithfulness and retrieval quality before generating answers. The generator synthesizes answers with citation anchors.

Evaluation

I use a query set plus regression runs for evaluation. Metrics include faithfulness checks against retrieved context, retrieval hit-rate, and citation coverage. Failure modes I track: retrieval misses, low confidence responses, and unsupported claims. Regression strategy: automated eval runs on each change catch issues before deployment.

Results

Improved traceability and reduced unsupported answers in eval runs. Clearer failure boundaries through abstain + citations. Latency and quality were tracked during eval runs; numbers will be published once benchmarked consistently.

Trade-offs & Lessons

Latency vs reliability: stricter evaluation gates increase latency but prevent failures. Strict retrieval boundaries prevented most hallucinations. The initial over-engineering of routing logic slowed iteration. Simplicity in evaluation gates mattered more than complex routing.

What I'd Improve Next

I'd add real-time feedback loops for routing accuracy. An A/B testing framework for retrieval strategies would help iterate faster. More granular evaluation metrics per domain would catch domain-specific regressions.

Project Info

Open Source: Yes

Status: Active