RAG Architect

RAG Architect

The agent designs, implements, and optimizes production-grade RAG pipelines, from document chunking through evaluation.

Core Capabilities

• Chunking strategy selection — match corpus characteristics to fixed-size, sentence, paragraph, semantic, recursive, or document-aware chunking with sized parameters.
• Embedding & vector-DB choice — pick an embedding model (local vs API) and vector store (Pinecone, Weaviate, Qdrant, Chroma, pgvector) by scale, latency, and cost.
• Retrieval design — dense, sparse (BM25), or hybrid retrieval with Reciprocal Rank Fusion plus cross-encoder reranking when precision must exceed 0.85.
• Query transformations — HyDE, multi-query, and step-back techniques for style mismatch and ambiguous queries.
• Guardrails — PII detection, hallucination/NLI checks, source attribution, confidence scoring, and injection prevention.
• Evaluation — RAGAS faithfulness/relevance plus IR metrics (Precision@K, Recall@K, MRR, NDCG) with failure analysis.
• Production patterns — caching, streaming, fallbacks, incremental re-indexing, and cost control.

When to Use

• Building a RAG system end to end.
• Selecting a chunking strategy or choosing a vector database.
• Optimizing retrieval quality or adding reranking.
• Evaluating a pipeline with RAGAS or IR metrics.

Clarify First

Before designing the pipeline, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Corpus characteristics — size, document structure, and domain (drives the chunking-strategy selection and parameters)
• Scale / latency / cost constraints — query volume and budget (selects the embedding model and vector DB)
• Retrieval precision target — the accuracy bar (precision >0.85 forces hybrid retrieval + cross-encoder reranking)
• Query types — ambiguous, multi-hop, or style-mismatched (decides which query transforms: HyDE / multi-query / step-back)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

Python tools live at the skill root (no scripts/ dir). Full flags/output formats: references/tool-cli-reference.md.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/rag-design-guide.md — the 8-step workflow, every selection matrix (chunking, embedding, vector DB, retrieval, query transforms), context-window optimization, RAGAS targets, guardrails, a worked YAML pipeline example, production patterns, common pitfalls, troubleshooting table, and success criteria. Read when designing or debugging a pipeline.
• references/tool-cli-reference.md — full flag/parameter tables, examples, and output formats for chunking_optimizer.py, retrieval_evaluator.py, and rag_pipeline_designer.py. Read before running the scripts.
• references/chunking_strategies_comparison.md — deep comparison of the five chunking strategies with size distributions, quality metrics, and domain recommendations. Read when choosing a chunking strategy.
• references/embedding_model_benchmark.md — benchmark of OpenAI, open-source, specialized, and domain-specific embedding models. Read when selecting an embedding model.
• references/rag_evaluation_framework.md — full evaluation framework: retrieval/generation/end-to-end dimensions, offline/online/human methodologies, metric implementations. Read when building an evaluation harness.

Scope & Limitations

This skill covers:

• End-to-end RAG pipeline architecture design: chunking, embedding, vector storage, retrieval, reranking, and evaluation.
• Quantitative chunking analysis across four strategy families (fixed-size, sentence, paragraph, semantic).
• Retrieval quality evaluation using standard IR metrics (Precision@K, Recall@K, MRR, NDCG) with a built-in TF-IDF baseline.
• Automated pipeline design with component selection, cost projection, and Mermaid architecture diagrams.

This skill does NOT cover:

• LLM prompt engineering or generation-side optimization -- see engineering/prompt-engineer-toolkit.
• Database schema design for metadata stores alongside vector databases -- see engineering/database-designer.
• Production observability, alerting, and SLO dashboards for deployed pipelines -- see engineering/observability-designer.
• Agent orchestration or multi-step reasoning workflows that sit on top of RAG retrieval -- see engineering/agent-workflow-designer.

Integration Points