> tpl_air_001
RAG Architecture Document & Production Specification
Production architecture specification for Retrieval-Augmented Generation (RAG) systems covering ingestion pipelines, chunking, hybrid retrieval, cross-encoder re-ranking, grounding policies, and the RAG Triad evaluation framework.
Comprehensive technical architecture blueprint for enterprise RAG and Agentic systems. Defines data ingestion CDC, chunking strategies, dense/lexical hybrid search algorithms, strict citation/grounding thresholds, and automated evaluation metrics to eliminate production hallucinations.
Important Tech Document Template & Operational Notice
TinyCTO.tv Tech Document Template Notice: This template is a general educational and operational starting point. It is not legal, tax, accounting, investment, procurement, regulatory, security or certification advice. Requirements vary by jurisdiction, organization, contract and risk. Review and adapt it with qualified professionals before relying on it.
Problem Solved
Prevents catastrophic production hallucinations, outdated grounding, leaky authorization boundaries across vector spaces, explosive token costs, and unmeasurable retrieval degradation in production GenAI deployments.
When to Use
- •Designing an enterprise conversational agent, technical documentation assistant, or semantic search engine.
- •Upgrading from a naive proof-of-concept RAG script to a high-scale production architecture.
- •Implementing strict compliance guardrails where LLM outputs must be verifiable against source ground truth.
- •Auditing vector database security, tenant data isolation, and retrieval latency budgets.
When NOT to Use
- •Simple stateless LLM classification, sentiment analysis, or raw text summarization tasks with no external knowledge.
- •Small in-memory prompts where entire context easily fits within modern 128k+ context windows without retrieval.
- •Pure structured SQL querying where text-to-SQL or direct relational reporting is more deterministic.
7 Template Sections & Structural Outline
Business motivation, knowledge corpus scope, and expected operational ROI.
Connectors, Change Data Capture (CDC), file parsing, and document deduplication.
Chunk sizes, overlap windows, recursive markdown splitting, and semantic boundary preservation.
Dense embedding selection, dimensional trade-offs, index type (HNSW vs IVFFlat), and cluster sharding.
Combining dense vector search with sparse BM25 keyword matching via Reciprocal Rank Fusion (RRF).
Mandatory evidence citation rules, attribution anchors, and polite refusal when context is missing.
Context Relevance, Groundedness (Faithfulness), and Answer Relevance automated benchmarks.
Completion Instructions
Independent Review Checklist
- Document ingestion includes automated file sanitization, OCR validation, and metadata tagging.
- Chunking boundaries preserve semantic headings and table structures without arbitrary token truncation.
- Vector database access enforces multi-tenant row-level security (RLS) and encryption at rest.
- Hybrid search combines dense embeddings with sparse BM25 to catch specific acronyms and part numbers.
- Re-ranking model latency fits within the allocated query response time budget (< 300ms).
- System prompt strictly enforces evidence citation with verifiable document anchors.
- Abstention policy triggers reliably when retrieved context similarity falls below the confidence cutoff.
- RAG Triad evaluation score is measured continuously against a versioned golden evaluation dataset.
Worked Example: FinGuard Regulatory Intelligence RAG Engine
Fictional Entity: FinGuard Compliance AI Technologies Inc. (Fictional Entity)
Production architecture for indexing 2.8 million pages of European banking regulations (EBA, ECB, GDPR, DORA) allowing risk officers to query complex requirements with strict clause-level legal citation and zero hallucination tolerance.
- •Document-aware hierarchical chunking preserves legal clause numbering and cross-references.
- •Hybrid search (BGE-M3 dense vectors + BM25) achieves 96.4% recall on article-specific queries.
- •Automated cross-encoder re-ranking drops latency from 1.4s to 420ms with token budget limits.
Frequently Asked Questions
Why is hybrid search (Dense + BM25) recommended over pure vector search?
Dense vector search excels at conceptual and semantic similarity, but frequently fails on exact keyword lookups like part numbers, error codes, legal statute numbers, and unique product acronyms. Hybrid search with Reciprocal Rank Fusion delivers the strengths of both.
What is the RAG Triad evaluation framework?
Developed by the AI research community (TruLens), the RAG Triad evaluates: 1. Context Relevance (did retrieval fetch documents relevant to the query?), 2. Groundedness (is the LLM answer strictly supported by the retrieved context?), and 3. Answer Relevance (does the generated answer actually address the user's question?).
How do we prevent cross-tenant data leaks in the vector database?
Never rely solely on embedding separation for data isolation. Enforce hard metadata filtering in every vector query (e.g. tenant_id == user.tenant_id) at the database index layer before vector distance calculation.
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Authoritative Sources
- NIST AI Risk Management Framework (AI RMF 1.0)NIST • OFFICIAL REQUIREMENT
- OWASP Top 10 for Large Language Model ApplicationsOWASP • OFFICIAL RECOMMENDATION
- Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksLewis et al. (Meta AI Research) • INDUSTRY PRACTICE