RAG Architect

The agent designs, implements, and optimizes production-grade Retrieval-Augmented Generation pipelines, covering the full lifecycle from document chunking through evaluation.

Workflow

1. Analyse corpus -- Profile the document collection: count, average length, format mix (PDF, HTML, Markdown), language(s), and domain. Validate that sample documents are accessible before proceeding.
2. Select chunking strategy -- Choose from the Chunking Strategy Matrix based on corpus characteristics. Set chunk size, overlap, and boundary rules. Run a test split on 100 sample documents.
3. Choose embedding model -- Select an embedding model from the Embedding Model table based on domain, latency budget, and cost constraints. Verify dimension compatibility with the target vector database.
4. Select vector database -- Pick a vector store from the Vector Database Comparison based on scale, query patterns, and operational requirements.
5. Design retrieval pipeline -- Configure retrieval strategy (dense, sparse, or hybrid). Add reranking if precision requirements exceed 0.85. Set the top-K parameter and similarity threshold.
6. Implement query transformations -- If query-document style mismatch exists, enable HyDE. If queries are ambiguous, enable multi-query generation. Validate each transformation improves retrieval metrics on a held-out set.
7. Configure guardrails -- Enable PII detection, toxicity filtering, hallucination detection, and source attribution. Set confidence score thresholds.
8. Evaluate end-to-end -- Run the RAGAS evaluation framework. Verify faithfulness > 0.90, context relevance > 0.80, answer relevance > 0.85. Iterate on weak components.

Chunking Strategy Matrix

Strategy	Best For	Chunk Size	Overlap	Pros	Cons
Fixed-size (token)	Uniform docs, consistent sizing	512-2048 tokens	10-20%	Predictable, simple	Breaks semantic units
Sentence-based	Narrative text, articles	3-8 sentences	1 sentence	Preserves language boundaries	Variable sizes
Paragraph-based	Structured docs, technical manuals	1-3 paragraphs	0-1 paragraph	Preserves topic coherence	Highly variable sizes
Semantic	Long-form, research papers	Dynamic	Topic-shift detection	Best coherence	Computationally expensive
Recursive	Mixed content types	Dynamic, multi-level	Per-level	Optimal utilization	Complex implementation
Document-aware	Multi-format collections	Format-specific	Section-level	Preserves metadata	Format-specific code required

Embedding Model Comparison

Model	Dimensions	Speed	Quality	Cost	Best For
all-MiniLM-L6-v2	384	~14K tok/s	Good	Free (local)	Prototyping, low-latency
all-mpnet-base-v2	768	~2.8K tok/s	Better	Free (local)	Balanced production use
text-embedding-3-small	1536	API	High	$0.02/1M tokens	Cost-effective production
text-embedding-3-large	3072	API	Highest	$0.13/1M tokens	Maximum quality
Domain fine-tuned	Varies	Varies	Domain-best	Training cost	Specialized domains (legal, medical)

Vector Database Comparison

Database	Type	Scaling	Key Feature	Best For
Pinecone	Managed	Auto-scaling	Metadata filtering, hybrid search	Production, managed preference
Weaviate	Open source	Horizontal	GraphQL API, multi-modal	Complex data types
Qdrant	Open source	Distributed	High perf, low memory (Rust)	Performance-critical
Chroma	Embedded	Limited	Simple API, SQLite-backed	Prototyping, small-scale
pgvector	PostgreSQL ext	PostgreSQL scaling	ACID, SQL joins	Existing PostgreSQL infra

Retrieval Strategies

Strategy	When to Use	Implementation
Dense (vector similarity)	Default for semantic search	Cosine similarity with k-NN/ANN
Sparse (BM25/TF-IDF)	Exact keyword matching needed	Elasticsearch or inverted index
Hybrid (dense + sparse)	Best of both needed	Reciprocal Rank Fusion (RRF) with tuned weights
+ Reranking	Precision must exceed 0.85	Cross-encoder reranker after initial retrieval

Query Transformation Techniques

Technique	When to Use	How It Works
HyDE	Query/document style mismatch	LLM generates hypothetical answer; embed that instead of query
Multi-query	Ambiguous queries	Generate 3-5 query variations; retrieve for each; deduplicate
Step-back	Specific questions needing general context	Transform to broader query; retrieve general + specific

Context Window Optimization

• Relevance ordering: Most relevant chunks first in the context window
• Diversity: Deduplicate semantically similar chunks
• Token budget: Fit within model context limit; reserve tokens for system prompt and answer
• Hierarchical inclusion: Include section summary before detailed chunks when available
• Compression: Summarize low-relevance chunks; extract key facts from verbose passages

Evaluation Metrics (RAGAS Framework)

Metric	Target	What It Measures
Faithfulness	> 0.90	Answers grounded in retrieved context
Context Relevance	> 0.80	Retrieved chunks relevant to query
Answer Relevance	> 0.85	Answer addresses the original question
Precision@K	> 0.70	% of top-K results that are relevant
Recall@K	> 0.80	% of relevant docs found in top-K
MRR	> 0.75	Reciprocal rank of first relevant result

Guardrails

• PII detection: Scan retrieved chunks and generated responses for PII; redact or block
• Hallucination detection: Compare generated claims against source documents via NLI
• Source attribution: Every factual claim must cite a retrieved chunk
• Confidence scoring: Return confidence level; if below threshold, return "I don't have enough information"
• Injection prevention: Sanitize user queries; reject prompt injection attempts

Example: Internal Knowledge Base RAG Pipeline

corpus:
  documents: 12,000 Confluence pages + 3,000 PDFs
  avg_length: 2,400 tokens
  languages: [English]
  domain: internal engineering docs

pipeline:
  chunking:
    strategy: recursive
    max_tokens: 512
    overlap: 50 tokens
    boundary: paragraph
  embedding:
    model: text-embedding-3-small
    dimensions: 1536
    batch_size: 100
  vector_db:
    engine: pgvector
    index: HNSW (ef_construction=128, m=16)
    reason: "Existing PostgreSQL infra; ACID compliance for audit"
  retrieval:
    strategy: hybrid
    dense_weight: 0.7
    sparse_weight: 0.3
    top_k: 10
    reranker: cross-encoder/ms-marco-MiniLM-L-12-v2
    final_k: 5

evaluation_results:
  faithfulness: 0.93
  context_relevance: 0.84
  answer_relevance: 0.88
  precision_at_5: 0.76
  recall_at_10: 0.85

Production Patterns

• Caching: Query-level (exact match), semantic (similar queries via embedding distance < 0.05), chunk-level (embedding cache)
• Streaming: Stream generation tokens while retrieval completes; show sources after generation
• Fallbacks: If primary vector DB is unavailable, serve from read-replica; if retrieval returns no results above threshold, say so explicitly
• Document refresh: Incremental re-embedding on change detection; full re-index weekly
• Cost control: Batch embeddings, cache aggressively, route simple queries to BM25 only

Common Pitfalls

Problem	Solution
Chunks break mid-sentence	Use boundary-aware chunking with sentence/paragraph overlap
Low retrieval precision	Add cross-encoder reranker; tune similarity threshold
High latency (> 2s)	Cache embeddings; use faster model; reduce top-K
Inconsistent quality	Implement RAGAS evaluation in CI; add quality scoring
Scalability bottleneck	Shard vector DB; implement auto-scaling; add caching layer

Scripts

Chunking Optimizer

Analyses corpus and recommends optimal chunking strategy with parameters.

Retrieval Evaluator

Runs evaluation suite (precision, recall, MRR, NDCG) against a test query set.

Pipeline Benchmarker

Measures end-to-end latency, throughput, and cost per query across configurations.