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.

Tool	Purpose	Command
chunking_optimizer.py	Analyze a corpus and recommend the optimal chunking strategy with parameters	python chunking_optimizer.py ./docs --output results.json
retrieval_evaluator.py	Evaluate retrieval with Precision@K, Recall@K, MRR, NDCG + failure analysis	python retrieval_evaluator.py queries.json ./corpus ground_truth.json
rag_pipeline_designer.py	Generate a full pipeline design, cost projection, and Mermaid diagram from requirements	python rag_pipeline_designer.py requirements.json --output pipeline_design.json

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

Skill	Integration	Data Flow
engineering/prompt-engineer-toolkit	Optimize system prompts and few-shot examples fed alongside retrieved chunks	Pipeline design output --> prompt templates that reference chunk format and metadata
engineering/database-designer	Design relational metadata stores (tags, access control, source tracking) paired with the vector database	Vector DB recommendation --> metadata schema for hybrid storage
engineering/observability-designer	Set up latency, throughput, and accuracy monitoring for the deployed RAG pipeline	Evaluation metrics and SLO targets --> dashboards and alerting rules
engineering/agent-workflow-designer	Embed the RAG retrieval step inside multi-agent reasoning workflows	Retrieval config --> agent tool definition with top-K and threshold parameters
engineering/ci-cd-pipeline-builder	Automate embedding re-indexing, evaluation regression tests, and deployment on document changes	Evaluation thresholds --> CI gate that blocks deploys when metrics regress
engineering/api-design-reviewer	Review the query and ingestion API surface exposed by the RAG service	Pipeline config --> OpenAPI spec review for search and ingest endpoints