Most RAG implementations are just embeddings shoved into a vector DB and called a day. Chunk strategy is arbitrary, retrieval is unfiltered, and the LLM gets garbage in and produces confident nonsense out. RAG-Architect brings structured retrieval design — chunking taxonomy, reranking pipelines, hybrid search, and freshness scoring — to your Claude-Code workflow.

The Hook

**TL;DR:** RAG-Architect treats retrieval as an engineering problem, not a magic embedding box. Chunk size, metadata taxonomy, hybrid search weighting, reranking — these decisions determine whether your RAG system answers questions correctly or just confidently. Get them wrong and you'll spend months wondering why your AI "doesn't know" things that are clearly in the documents.

The average RAG demo works beautifully. The average production RAG system is a quiet disaster — retrieving semantically similar but contextually wrong documents, losing critical information to aggressive chunking, and silently hallucinating answers that sound authoritative because the retrieval step failed invisibly. RAG-Architect makes the invisible visible.

The 10-Second Pitch

**Chunking strategy framework** — Recursive, semantic, and agentic chunking with clear tradeoffs
**Metadata taxonomy design** — Document provenance, recency, authority, and access control tagging
**Hybrid search pipeline** — Combines dense vector plus sparse BM25 retrieval with learned fusion
**Reranking integration** — Cross-encoder reranking before context injection
**Freshness scoring** — Time-decay weighting so outdated docs don't outrank current ones
**Retrieval evaluation suite** — Hit rate, MRR, and answer quality benchmarks per query type

Setup Directions

Step 1 — Audit Your Document Corpus

Use rag-architect to audit our current knowledge base. Analyze the document corpus in ./docs: assess chunk boundaries, identify metadata gaps, and score retrieval quality for our top-20 most common query types. Produce a chunking strategy recommendation and a retrieval benchmark baseline.

Step 2 — Configure Hybrid Search

retrieval:

dense:

provider: pinecone

model: text-embedding-3-large

dimension: 3072

sparse:

type: bm25

k1: 1.5

b: 0.75

fusion:

method: rrf

k: 60

rerank:

model: cross-encoder/ms-marco-MiniLM-L-12v2

top_k: 20

final_k: 5

freshness:

decay: logarithmic

half_life_days: 90

Step 3 — Run Baseline Evaluation

rag-architect eval --config ./rag-config.yaml \

--queries ./test-queries.jsonl \

--metrics hit_rate,mrr,answer_faithfulness

Step 4 — Iterate Based on Report

The evaluation output includes per-query retrieval traces — you can see exactly which documents were retrieved, their fusion scores, and whether the final answer was faithful to them.

Pros / Cons

Pros	Cons
End-to-end retrieval benchmarking — no more flying blind	Requires labeled evaluation queries — hard to benchmark without ground truth

Hybrid search outperforms pure vector search on most domains	Reranking adds latency (typically 50-200ms per query)

Chunking strategy framework prevents "everything at 512 tokens"	Freshness scoring needs reliable document timestamps

Composable with any vector DB	Cross-encoder reranking requires a hosted model endpoint

Verdict

RAG is only as good as its retrieval layer, and retrieval is only as good as its design. The most common mistake is treating embedding quality as the bottleneck when chunking strategy, metadata taxonomy, and fusion method are doing more damage. RAG-Architect makes you prove your retrieval works before you trust your LLM to answer from it.

**Rating: Essential for any production RAG system. Game-changing for systems where answer accuracy is a hard requirement.**

#rag #retrieval #vector-database #embeddings #claude-code

Detects hallucination risk from retrieval failure	Chunking optimization is domain-specific

RAG-Architect: Building Retrieval Systems That Actually Retrieve What You Need