# Techwavedev Agi Agent Kit RAG Engineer > RAG Engineer - Skill: `tomevault-io/techwavedev-agi-agent-kit-rag-engineer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/techwavedev-agi-agent-kit-rag-engineer` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/techwavedev-agi-agent-kit-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/techwavedev-agi-agent-kit-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 ``` ## 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` ## When to Use This skill is applicable to execute the workflow or actions described in the overview. --- ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency. ```bash # Check for prior AI agent orchestration context before starting python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Rag Engineer" ``` ### Storing Results After completing work, store AI agent orchestration decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \ --type decision --project \ --tags rag-engineer ai-agents ``` ### Multi-Agent Collaboration This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work. ```bash python3 execution/cross_agent_context.py store \ --agent "" \ --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \ --project ``` ### Control Tower Integration Register agents and tasks with the Control Tower (`execution/control_tower.py`) for centralized orchestration across machines and LLM providers. ### Blockchain Identity Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.