# Whd4 Expert AI Skills RAG Engineer > RAG Engineer - Skill: `tomevault-io/whd4-expert-ai-skills-rag-engineer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/whd4-expert-ai-skills-rag-engineer` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/whd4-expert-ai-skills-rag-engineer/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/whd4-expert-ai-skills-rag-engineer --- # RAG Engineer **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. ## Capabilities - Vector embeddings and similarity search - Document chunking and preprocessing - Retrieval pipeline design - Semantic search implementation - Context window optimization - Hybrid search (keyword + semantic) ## Requirements - LLM fundamentals - Understanding of embeddings - Basic NLP concepts ## Patterns ### Semantic Chunking Chunk by meaning, not arbitrary token counts ```javascript - 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 ```javascript - 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 ```javascript - BM25/TF-IDF for keyword matching - Vector similarity for semantic matching - Reciprocal Rank Fusion for combining scores - Weight tuning based on query type ``` ## Advanced Retrieval Patterns (Expert) ### 1. Query Routing & Decomposition Don't use one tool for everything. - **Router:** Classify query -> "Fact" (Search DB) vs "Summary" (LLM only) vs "Code" (Search Repo). - **Decomposition:** Break "Compare X and Y" into "Get X", "Get Y", "Compare". ### 2. Hypothethical Document Embeddings (HyDE) Embeddings map *answers* close to *answers*, not *questions* to *answers*. - **Step 1:** Ask LLM to hallucinate a fake answer to the user query. - **Step 2:** Embed that fake answer. - **Step 3:** Retrieve real documents close to the fake answer. ### 3. Recursive Retrieval (Parent-Child) - **Index:** Small chunks (sentences) for accurate matching. - **Retrieve:** The parent chunk (paragraph/page) for context. - **Result:** High precision match + High context generation. ## 📏 Evaluation (RAGAS Framework) **Don't guess. Measure.** | Metric | Measures | High Means... | |--------|----------|---------------| | **Faithfulness** | Does answer come *only* from context? | No hallucinations. | | **Answer Relevance** | Does answer address the query? | User is happy. | | **Context Precision** | Did we retrieve relevant chunks? | High signal-to-noise. | | **Context Recall** | Did we retrieve *all* needed chunks? | No missing info. | ## 🏭 Production RAG (War Stories) - **Caching:** Cache specific queries and "Head" queries (top 20%). Semantically cache similar queries. - **Indexing:** HNSW (Fast, RAM heavy) vs IVF (Slower, Disk/SSD friendly). Use HNSW for <10M vectors. - **Reranking:** Vector search is "fuzzy". Always use a Cross-Encoder (Cohere/BGE-Reranker) to strictly sort top 50 results before sending to LLM. ## Anti-Patterns ### ❌ Fixed Chunk Size ### ❌ Embedding Everything ### ❌ Ignoring Evaluation ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Fixed-size chunking breaks sentences and context | high | Use semantic chunking that respects document structure: | | Pure semantic search without metadata pre-filtering | medium | Implement hybrid filtering: | | Using same embedding model for different content types | medium | Evaluate embeddings per content type: | | Using first-stage retrieval results directly | medium | Add reranking step: | | Cramming maximum context into LLM prompt | medium | Use relevance thresholds: | | Not measuring retrieval quality separately from generation | high | Separate retrieval evaluation: | | Not updating embeddings when source documents change | medium | Implement embedding refresh: | | Same retrieval strategy for all query types | medium | Implement hybrid search: | ## Related Skills Works well with: `ai-agents-architect`, `prompt-engineer`, `database-architect`, `backend` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/whd4) — claim your Tome and manage your conversions.