RAG Implementer

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.

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⚠️ 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.

Cost Reality Check

Naive RAG (Prototype):

• Time: 1-2 weeks
• Cost: $50-150/month (vector DB + embeddings + API calls)
• When: Prototype, <10k documents, proof of concept

Advanced RAG (Production):

• Time: 3-4 weeks
• Cost: $200-500/month (hybrid search, re-ranking, monitoring)
• When: Production, 10k-1M documents, validated demand