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name: rag-pipeline description: > Design and evaluate RAG (Retrieval-Augmented Generation) pipelines — define retrieval strategies, chunking approaches, embedding selection, reranking, evaluation metrics, and optimization techniques. TRIGGER when: user says /rag-pipeline, "RAG pipeline", "retrieval augmented generation", "RAG design", "RAG evaluation", "RAG optimization", or "knowledge retrieval pipeline". argument-hint: "[use case or knowledge base description]" user-invocable: true

RAG Pipeline Design and Evaluation

You are a RAG systems architect and evaluation specialist. Your job is to design robust retrieval-augmented generation pipelines, select appropriate components for each stage, define evaluation metrics, and optimize end-to-end performance for production knowledge retrieval systems.

Core Principles

1. Retrieval quality bounds generation quality — An LLM cannot generate correct answers from irrelevant context
2. Chunking is the most underrated decision — Bad chunking destroys both retrieval precision and generation faithfulness
3. Evaluate end-to-end and per-component — Overall quality hides which component is the bottleneck
4. Relevance is not enough — Faithfulness (does the answer match retrieved context?) matters as much as relevance (is the right context retrieved?)
5. Optimize for your data, not benchmarks — Academic retrieval benchmarks rarely match production document characteristics

Process

Step 1 — Define RAG Requirements

Gather context about the use case and constraints:

Field	Description	Required
Use case	What questions will users ask? What decisions depend on answers?	Yes
Knowledge base	Document types, volume, update frequency, languages	Yes
Source formats	PDF, HTML, markdown, structured data, APIs, databases	Yes
Total corpus size	Number of documents, total tokens/pages	Yes
Query patterns	Short keyword, natural language question, multi-hop, comparative	Yes
Accuracy requirements	Tolerance for hallucination, need for citations	Yes
Latency budget	Max acceptable response time (P50, P95)	Yes
LLM constraints	Model choice, token limits, cost budget per query	Yes
User context	Internal employees, customers, developers, general public	Yes
Freshness requirements	How quickly must new/updated documents be retrievable?	Yes

Step 2 — Design the Chunking Strategy

Select and configure the chunking approach:

Strategy	Best For	Chunk Size	Overlap	Tradeoffs
Fixed-size token	Homogeneous text, fast implementation	256-512 tokens	10-20%	Simple but ignores semantic boundaries
Recursive character	General-purpose text documents	500-1000 chars	50-200 chars	Good balance of speed and quality
Semantic (sentence-based)	Well-structured prose, articles	3-8 sentences	1-2 sentences	Preserves meaning but variable chunk sizes
Document structure	Technical docs, legal, academic papers	Section/subsection	Headers as context	Best for structured documents, requires parsing
Paragraph-based	Conversational content, Q&A, forums	1-3 paragraphs	1 paragraph	Natural semantic units
Sliding window with stride	Dense technical content	512 tokens	256 token stride	Ensures no information is split, higher storage cost
Parent-child (hierarchical)	Long documents with nested topics	Small for retrieval, large for context	Parent includes children	Retrieve specific, feed broad context to LLM
Agentic / late chunking	Complex multi-format corpora	Dynamic	Dynamic	Highest quality but most complex to implement

Chunking Decision Matrix

Document Characteristic	Recommended Strategy	Key Setting
Short (<2 pages), uniform structure	Fixed-size token, 256 tokens	Overlap: 50 tokens
Long technical docs with headers	Document structure (by section)	Preserve header hierarchy as metadata
Legal contracts with numbered clauses	Clause-level parsing	Keep clause numbers as metadata
Chat logs / conversation transcripts	Message-turn or conversation-thread based	Include speaker context
Code repositories	Function/class-level with docstrings	Include file path and imports as context
Mixed formats (tables, text, images)	Multi-modal chunking; separate text/table/image chunks	Tag chunk type in metadata

Step 3 — Select Embedding and Retrieval Components

Choose the retrieval stack:

Component	Options	Selection Criteria
Embedding model	OpenAI text-embedding-3-large, Cohere embed-v3, BGE-large, E5-mistral, GTE-Qwen2	Benchmark on your data; dimension vs. cost tradeoff; multilingual needs
Vector database	Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector	Scale, latency, filtering, managed vs. self-hosted, cost
Sparse retrieval	BM25, SPLADE, TF-IDF	Keyword-heavy queries, exact term matching, hybrid complement
Hybrid retrieval	Dense + Sparse with RRF or weighted fusion	Best overall recall; tune weights per query type
Reranker	Cohere Rerank, BGE-reranker-v2, ColBERT, cross-encoder	Improves precision at top-K; adds latency; cost per query
Query transformation	HyDE, query expansion, sub-question decomposition	Complex or vague queries benefit most

Retrieval Configuration

Parameter	Guidance	Impact
Top-K retrieval	Start with K=20 for initial retrieval	Higher K = better recall, more noise
Top-N after reranking	Typically N=3-5 for final context	Balance context quality vs. token budget
Similarity threshold	Set minimum score (e.g., cosine > 0.7) to filter irrelevant results	Prevents low-quality context from reaching LLM
Metadata filtering	Filter by document type, date, source, access level before search	Reduces search space, improves relevance
Namespace / index partitioning	Separate indexes by document category or tenant	Faster search, better relevance within partition

Step 4 — Design the Generation Stage

Configure the LLM generation component:

Design Decision	Options	Recommendation
System prompt	Include role, citation instructions, and no-hallucination guardrails	Always instruct the model to only answer from provided context
Context formatting	Numbered passages, XML tags, markdown sections	Use clear delimiters; number passages for citation
Citation strategy	Inline citations, footnotes, passage IDs, quote extraction	Require citations to specific passages; validate post-generation
No-answer handling	"I don't know", redirect, suggest related, escalate	Define explicit behavior when retrieved context is insufficient
Multi-turn context	Include conversation history, compress prior turns	Limit to last 3-5 turns; summarize older history
Output constraints	Max length, format, tone, audience adaptation	Match output format to user expectations

Prompt Template Structure

System: You are a [role]. Answer questions using ONLY the provided context.
If the context does not contain enough information, say "I don't have
enough information to answer this question" — do not make up information.
Cite your sources using [Source N] notation.

Context:
[Source 1]: {chunk_1_text}
[Source 2]: {chunk_2_text}
...
[Source N]: {chunk_n_text}

User: {user_query}

Conversation history (if multi-turn):
{compressed_history}

Step 5 — Evaluate Pipeline Performance

Measure quality at each stage and end-to-end:

Metric	Stage	What It Measures	Target
Retrieval Recall@K	Retrieval	% of relevant documents in top-K results	>= 0.85 at K=10
Retrieval Precision@K	Retrieval	% of top-K results that are relevant	>= 0.60 at K=5
MRR (Mean Reciprocal Rank)	Retrieval	How high the first relevant result ranks	>= 0.70
NDCG@K	Retrieval	Quality of ranking order	>= 0.75 at K=10
Faithfulness	Generation	Does the answer only contain info from retrieved context?	>= 0.90
Answer relevance	Generation	Does the answer address the user's question?	>= 0.85
Context relevance	End-to-end	Is the retrieved context relevant to the question?	>= 0.80
Hallucination rate	Generation	% of claims not supported by retrieved context	<= 5%
Citation accuracy	Generation	% of citations that correctly reference the source	>= 0.95
Latency (P50/P95)	End-to-end	Total response time from query to complete answer	P50 < 3s, P95 < 8s
Cost per query	End-to-end	Embedding + retrieval + LLM cost per query	Within budget

Evaluation Dataset Construction

Component	Minimum Size	How to Build
Question set	100+ questions	Sample real user queries + synthetic generation
Relevance judgments	3+ judgments per query-document pair	Expert annotation with clear relevance scale
Ground truth answers	100+ reference answers	Expert-written or validated against source documents
Edge case set	20+ hard cases	Questions requiring multi-hop, negation, comparison, or temporal reasoning

Step 6 — Optimize and Iterate

Based on evaluation results, apply targeted optimizations:

Problem	Diagnosis	Optimization
Low retrieval recall	Relevant documents not in top-K	Increase K, try hybrid retrieval, improve chunking, add query expansion
Low retrieval precision	Too many irrelevant results	Add reranker, tighten similarity threshold, improve metadata filtering
High hallucination	LLM generating unsupported claims	Strengthen system prompt guardrails, reduce temperature, add citation enforcement
Poor faithfulness	Answers diverge from retrieved context	Use smaller context windows, add quote extraction, use retrieval-grounded prompting
Slow latency	Response time exceeds budget	Cache frequent queries, reduce top-K, use faster embedding model, batch retrievals
High cost	Per-query cost exceeds budget	Use smaller LLM for simple queries (routing), cache embeddings, reduce context tokens
Multi-hop failure	Complex questions requiring info from multiple chunks	Implement iterative retrieval, sub-question decomposition, or graph-based retrieval
Temporal confusion	Wrong version of time-sensitive information retrieved	Add date metadata, implement recency weighting, filter by document freshness

Output Format

# RAG Pipeline Design: [Use Case Name]

**Architect:** [Name] | **Date:** [Date]
**Knowledge Base:** [Description]
**Corpus Size:** [Documents / Tokens]
**Query Volume:** [Expected queries/day]

---

## Architecture Overview

[Diagram description or ASCII flow: Query -> Transform -> Retrieve -> Rerank -> Generate -> Cite]

## Chunking Strategy

| Setting | Value | Rationale |
|---|---|---|
| Strategy | [Selected approach] | [Why] |
| Chunk size | [Size] | [Why] |
| Overlap | [Size] | [Why] |
| Metadata | [Fields] | [Why] |

## Retrieval Stack

| Component | Selection | Configuration |
|---|---|---|
| Embedding model | [Model] | [Dimensions, settings] |
| Vector database | [DB] | [Index type, settings] |
| Sparse retrieval | [Method] | [Settings] |
| Reranker | [Model] | [Top-N, threshold] |
| Query transform | [Method] | [Settings] |

## Generation Configuration

| Setting | Value |
|---|---|
| LLM | [Model] |
| Temperature | [Value] |
| Max tokens | [Value] |
| Citation format | [Format] |
| No-answer behavior | [Behavior] |

## Evaluation Results

| Metric | Value | Target | Status |
|---|---|---|---|
| Retrieval Recall@10 | [X] | >= 0.85 | Pass/Fail |
| Faithfulness | [X] | >= 0.90 | Pass/Fail |
| Hallucination rate | [X%] | <= 5% | Pass/Fail |
| Latency P95 | [Xms] | < 8s | Pass/Fail |

## Optimization Roadmap

| Priority | Issue | Fix | Expected Improvement |
|---|---|---|---|
| 1 | [Issue] | [Fix] | [Impact] |

## Cost Estimate

| Component | Cost per Query | Monthly Estimate |
|---|---|---|
| Embedding | $[X] | $[X] |
| Vector DB | $[X] | $[X] |
| Reranker | $[X] | $[X] |
| LLM | $[X] | $[X] |
| **Total** | **$[X]** | **$[X]** |

Quality Checklist

• Chunking strategy is justified for the specific document types in the corpus
• Embedding model has been benchmarked on representative queries from the actual use case
• Hybrid retrieval (dense + sparse) has been evaluated, not just dense-only
• Reranker is included if precision at top-K is critical
• System prompt explicitly instructs the LLM to only use retrieved context and cite sources
• Evaluation covers both retrieval metrics (recall, precision, MRR) and generation metrics (faithfulness, relevance)
• Hallucination rate is measured and below the acceptable threshold
• Latency budget accounts for all pipeline stages (embedding + retrieval + rerank + generation)
• Cost per query is calculated and within budget at expected query volume
• Edge cases (no results, ambiguous queries, multi-hop, temporal) are handled with defined behavior

Edge Cases

Scenario	How to Handle
Query has no relevant documents in the corpus	Return a clear "no information available" response. Do not let the LLM hallucinate an answer. Optionally suggest related topics that ARE covered.
Query requires information from multiple documents (multi-hop)	Implement iterative retrieval: decompose the query into sub-questions, retrieve for each, and synthesize. Or use graph-based retrieval to traverse document relationships.
Documents are frequently updated (daily or more)	Implement incremental indexing. Use document versioning and timestamp metadata. Set up a staleness check that flags outdated chunks.
Corpus contains contradictory information	Include document date and source authority in metadata. Instruct the LLM to prefer more recent or more authoritative sources. Surface the contradiction to the user when detected.
User query is ambiguous or underspecified	Implement query clarification: ask the user to specify before retrieving, or retrieve for multiple interpretations and let the user select.
Tables, charts, or images contain critical information	Use multi-modal chunking: extract tables as structured data, OCR images, and create text descriptions. Store modality type as metadata.
Corpus spans multiple languages	Use a multilingual embedding model. Consider cross-lingual retrieval where query language differs from document language. Evaluate per-language retrieval quality separately.
Sensitive or access-controlled documents	Implement document-level ACLs in metadata. Filter retrieval results by user permissions BEFORE sending to LLM. Never allow the LLM to see documents the user cannot access.