Build Retrieval-Augmented Generation systems: embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. USE WHEN designing or tuning a RAG retrieval pipeline for an LLM app.
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Build Retrieval-Augmented Generation systems: embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. USE WHEN designing or tuning a RAG retrieval pipeline for an LLM app.
cluster
ai-agents-meta
version
1.0.0
origin
antigravity-awesome-skills (MIT)
risk
unknown
source
vibeship-spawner-skills (Apache 2.0)
date_added
"2026-02-27T00:00:00.000Z"
RAG Engineer
Expert in building Retrieval-Augmented Generation systems. Masters embedding models,
vector databases, chunking strategies, and retrieval optimization for LLM applications.
Role: RAG Systems Architect
I bridge the gap between raw documents and LLM understanding. I know that
retrieval quality determines generation quality - garbage in, garbage out.
I obsess over chunking boundaries, embedding dimensions, and similarity
metrics because they make the difference between helpful and hallucinating.
Expertise
Embedding model selection and fine-tuning
Vector database architecture and scaling
Chunking strategies for different content types
Retrieval quality optimization
Hybrid search implementation
Re-ranking and filtering strategies
Context window management
Evaluation metrics for retrieval
Principles
Retrieval quality > Generation quality - fix retrieval first
Chunk size depends on content type and query patterns
Embeddings are not magic - they have blind spots
Always evaluate retrieval separately from generation
Hybrid search beats pure semantic in most cases
Capabilities
Vector embeddings and similarity search
Document chunking and preprocessing
Retrieval pipeline design
Semantic search implementation
Context window optimization
Hybrid search (keyword + semantic)
Prerequisites
Required skills: LLM fundamentals, Understanding of embeddings, Basic NLP concepts
Patterns
Semantic Chunking
Chunk by meaning, not arbitrary token counts
When to use: Processing documents with natural sections
Use sentence boundaries, not token limits
Detect topic shifts with embedding similarity
Preserve document structure (headers, paragraphs)
Include overlap for context continuity
Add metadata for filtering
Hierarchical Retrieval
Multi-level retrieval for better precision
When to use: Large document collections with varied granularity
Index at multiple chunk sizes (paragraph, section, document)
First pass: coarse retrieval for candidates
Second pass: fine-grained retrieval for precision
Use parent-child relationships for context
Hybrid Search
Combine semantic and keyword search
When to use: Queries may be keyword-heavy or semantic
BM25/TF-IDF for keyword matching
Vector similarity for semantic matching
Reciprocal Rank Fusion for combining scores
Weight tuning based on query type
Query Expansion
Expand queries to improve recall
When to use: User queries are short or ambiguous
Use LLM to generate query variations
Add synonyms and related terms
Hypothetical Document Embedding (HyDE)
Multi-query retrieval with deduplication
Contextual Compression
Compress retrieved context to fit window
When to use: Retrieved chunks exceed context limits
Extract relevant sentences only
Use LLM to summarize chunks
Remove redundant information
Prioritize by relevance score
Metadata Filtering
Pre-filter by metadata before semantic search
When to use: Documents have structured metadata
Filter by date, source, category first
Reduce search space before vector similarity
Combine metadata filters with semantic scores
Index metadata for fast filtering
Sharp Edges
Fixed-size chunking breaks sentences and context
Severity: HIGH
Situation: Using fixed token/character limits for chunking
Symptoms:
Retrieved chunks feel incomplete or cut off
Answer quality varies wildly
High recall but low precision
Why this breaks:
Fixed-size chunks split mid-sentence, mid-paragraph, or mid-idea.
The resulting embeddings represent incomplete thoughts, leading to
poor retrieval quality. Users search for concepts but get fragments.
Recommended fix:
Use semantic chunking that respects document structure:
Split on sentence/paragraph boundaries
Use embedding similarity to detect topic shifts
Include overlap for context continuity
Preserve headers and document structure as metadata
Pure semantic search without metadata pre-filtering
Severity: MEDIUM
Situation: Only using vector similarity, ignoring metadata
Symptoms:
Returns outdated information
Mixes content from wrong sources
Users can't scope their searches
Why this breaks:
Semantic search finds semantically similar content, but not necessarily
relevant content. Without metadata filtering, you return old docs when
user wants recent, wrong categories, or inapplicable content.
Recommended fix:
Implement hybrid filtering:
Pre-filter by metadata (date, source, category) before vector search
Post-filter results by relevance criteria
Include metadata in the retrieval API
Allow users to specify filters
Using same embedding model for different content types
Severity: MEDIUM
Situation: One embedding model for code, docs, and structured data
Symptoms:
Code search returns irrelevant results
Domain terms not matched properly
Similar concepts not clustered
Why this breaks:
Embedding models are trained on specific content types. Using a text
embedding model for code, or a general model for domain-specific
content, produces poor similarity matches.
Recommended fix:
Evaluate embeddings per content type:
Use code-specific embeddings for code (e.g., CodeBERT)
Consider domain-specific or fine-tuned embeddings
Benchmark retrieval quality before choosing
Separate indices for different content types if needed
Using first-stage retrieval results directly
Severity: MEDIUM
Situation: Taking top-K from vector search without reranking
Symptoms:
Clearly relevant docs not in top results
Results order seems arbitrary
Adding more results helps quality
Why this breaks:
First-stage retrieval (vector search) optimizes for recall, not precision.
The top results by embedding similarity may not be the most relevant
for the specific query. Cross-encoder reranking dramatically improves
precision for the final results.
Recommended fix:
Add reranking step:
Retrieve larger candidate set (e.g., top 20-50)
Rerank with cross-encoder (query-document pairs)
Return reranked top-K (e.g., top 5)
Cache reranker for performance
Cramming maximum context into LLM prompt
Severity: MEDIUM
Situation: Using all retrieved context regardless of relevance
Symptoms:
Answers drift with more context
LLM ignores key information
High token costs
Why this breaks:
More context isn't always better. Irrelevant context confuses the LLM,
increases latency and cost, and can cause the model to ignore the
most relevant information. Models have attention limits.
Recommended fix:
Use relevance thresholds:
Set minimum similarity score cutoff
Limit context to truly relevant chunks
Summarize or compress if needed
Order context by relevance
Not measuring retrieval quality separately from generation
Severity: HIGH
Situation: Only evaluating end-to-end RAG quality
Symptoms:
Can't diagnose poor RAG performance
Prompt changes don't help
Random quality variations
Why this breaks:
If answers are wrong, you can't tell if retrieval failed or generation
failed. This makes debugging impossible and leads to wrong fixes
(tuning prompts when retrieval is the problem).
Recommended fix:
Separate retrieval evaluation:
Create retrieval test set with relevant docs labeled
Measure MRR, NDCG, Recall@K for retrieval
Evaluate generation only on correct retrievals
Track metrics over time
Not updating embeddings when source documents change
Severity: MEDIUM
Situation: Embeddings generated once, never refreshed
Symptoms:
Returns outdated information
References deleted content
Inconsistent with source
Why this breaks:
Documents change but embeddings don't. Users retrieve outdated content
or, worse, content that no longer exists. This erodes trust in the
system.
Recommended fix:
Implement embedding refresh:
Track document versions/hashes
Re-embed on document change
Handle deleted documents
Consider TTL for embeddings
Same retrieval strategy for all query types
Severity: MEDIUM
Situation: Using pure semantic search for keyword-heavy queries
Symptoms:
Exact term searches miss results
Concept searches too literal
Users frustrated with both
Why this breaks:
Some queries are keyword-oriented (looking for specific terms) while
others are semantic (looking for concepts). Pure semantic search fails
on exact matches; pure keyword search fails on paraphrases.
Recommended fix:
Implement hybrid search:
BM25/TF-IDF for keyword matching
Vector similarity for semantic matching
Reciprocal Rank Fusion to combine
Tune weights based on query patterns
Related Skills
Works well with: ai-agents-architect, prompt-engineer, database-architect, backend
When to Use
User mentions or implies: building RAG
User mentions or implies: vector search
User mentions or implies: embeddings
User mentions or implies: semantic search
User mentions or implies: document retrieval
User mentions or implies: context retrieval
User mentions or implies: knowledge base
User mentions or implies: LLM with documents
User mentions or implies: chunking strategy
User mentions or implies: pinecone
User mentions or implies: weaviate
User mentions or implies: chromadb
User mentions or implies: pgvector
Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
