Agent skill · product strategy · daffy0208
RAG Implementer
Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.
Why this skill is useful
Provides domain-specific knowledge on implementing retrieval-augmented generation systems, including embedding strategies and evaluation methods.
What it needs
About 8k tokens when loaded. Last updated 2025-12-25. 33 stars on the source repository.
What this skill does
RAG Implementer Build production-ready retrieval-augmented generation systems. Core Principle RAG = Retrieval + Context Assembly + Generation Use RAG when you need LLMs to access fresh, domain-specific, or proprietary knowledge that wasn't in their training data. --- ⚠️ Prerequisites & Cost Reality Check STOP: Have You Validated the Need for RAG? Before implementing RAG, confirm: [ ] Problem validated - Completed product-strategist Phase 1 (problem discovery) [ ] Users need AI search - Tested with simpler alternatives (see below) [ ] ROI justified - Calculated cost vs benefit of RAG vs alternatives Try These FIRST (Before RAG) RAG is powerful but expensive. Try cheaper alternatives first: 1. FAQ Page / Documentation (1 day, $0) Create well-organized FAQ or docs Add search with Cmd+F Works for: <50 common questions, static content Test: Do users find answers? If yes, stop here. 2. Simple Keyword Search (2-3 days, $0-20/month) Use Algolia, Typesense, or PostgreSQL full-text search Good enough for 80% of use cases Works for: <100k documents, keyword matching sufficient Test: Do users get relevant results? If yes, stop here. 3. Manual Curation (Concierge MVP) (1 week, $0) Manually answer user questions Build FAQ from common questions Works for: <100 users, validating if users want AI Test: Do users value your answers enough to pay? If yes, consider RAG. 4. Simple Semantic Search (1 week, $30-50/month) Use OpenAI embeddings + Postgres pgvector Skip complex retrieval, re-ranking, etc. Works for: <50k documents, basic semantic search Test: Are embeddings better than keyword search? If no, stop here. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills daffy0208/rag-implementer