RAG Decision Framework
Strategic guide for choosing when to use RAG vs alternatives like fine-tuning or prompt engineering.
Overview
As a technical leader, you need to decide whether RAG is the right solution for your use case. This guide provides a systematic framework for making that decision.
The Core Question
When should you use RAG instead of alternatives?
RAG is ideal when you need to:
- Provide LLMs with up-to-date information not in their training data
- Access proprietary or domain-specific knowledge
- Ensure factual accuracy with citations
- Update knowledge without retraining models
- Reduce hallucinations in critical applications
Decision Tree
Does your LLM need external knowledge?
├─ NO → Use prompt engineering or fine-tuning
└─ YES → Continue
│
Does the knowledge change frequently?
├─ NO → Consider fine-tuning
└─ YES → RAG is likely the best choice
│
Do you need citations/sources?
├─ YES → RAG (with source tracking)
└─ NO → Evaluate cost vs fine-tuning
RAG vs Alternatives
RAG vs Fine-Tuning
| Criteria | RAG | Fine-Tuning |
|---|---|---|
| Knowledge Updates | Instant (update vector DB) | Requires retraining |
| Cost | Ongoing retrieval costs | High upfront, low ongoing |
| Accuracy | High with good retrieval | High with good data |
| Citations | Built-in source tracking | Not available |
| Latency | +50-200ms for retrieval | No additional latency |
| Best For | Dynamic knowledge, compliance | Behavior/style changes |
Use RAG when:
- Knowledge changes weekly/monthly
- You need to cite sources
- You have limited ML expertise
- Compliance requires audit trails
Use Fine-Tuning when:
- Knowledge is static
- You need to change model behavior/tone
- Latency is critical
- You have ML engineering resources
RAG vs Prompt Engineering
| Criteria | RAG | Prompt Engineering |
|---|---|---|
| Context Limit | Unlimited (via retrieval) | Limited to context window |
| Complexity | Requires infrastructure | Simple, immediate |
| Cost | Higher (retrieval + LLM) | Lower (LLM only) |
| Maintenance | Moderate (vector DB) | Low (just prompts) |
| Best For | Large knowledge bases | Small, static instructions |
Use RAG when:
- Knowledge exceeds context window (>100k tokens)
- You have >1000 documents
- Knowledge is structured (docs, wikis, databases)
Use Prompt Engineering when:
- Instructions fit in context window
- Knowledge is minimal
- Speed to market is critical
Hybrid Approaches
Many production systems combine multiple techniques:
RAG + Fine-Tuning
- Fine-tune for domain language and response style
- RAG for factual knowledge retrieval
- Example: Legal AI that speaks like a lawyer (fine-tuned) but retrieves case law (RAG)
RAG + Prompt Engineering
- Prompt engineering for task instructions
- RAG for knowledge retrieval
- Example: Customer support bot with retrieval-augmented responses
Cost Considerations
RAG Cost Structure
- Embedding costs: $0.0001-0.0004 per 1K tokens (one-time per document)
- Vector DB: $50-500/month (depends on scale)
- Retrieval: ~$0.0001 per query
- LLM with context: $0.001-0.03 per 1K tokens
Break-Even Analysis
RAG becomes cost-effective when:
- You have >10,000 documents
- Knowledge updates >1x per month
- You need to serve >1,000 queries/day
Risk Assessment
RAG Risks
- Retrieval failures: Wrong documents retrieved
- Context stuffing: Too much irrelevant information
- Latency: Additional 50-200ms per query
- Complexity: More moving parts to maintain
Mitigation Strategies
- Implement evaluation systems (see Evaluation Guide)
- Use reranking to improve relevance
- Monitor retrieval quality metrics
- Plan for fallback strategies
Success Criteria
Define success metrics before implementation:
Technical Metrics
- Retrieval accuracy: >90% relevant documents in top-5
- End-to-end latency: <2 seconds
- Answer accuracy: >85% factually correct
- Uptime: >99.9%
Business Metrics
- User satisfaction: >4.5/5 rating
- Cost per query: <$0.01
- Time to update knowledge: <1 hour
- Support ticket reduction: >30%
Common Use Cases
✅ Excellent Fit for RAG
- Customer support: FAQ retrieval, documentation search
- Internal knowledge: Company wikis, policies, procedures
- Research assistance: Scientific papers, legal documents
- Code assistance: Codebase search, API documentation
⚠️ Consider Alternatives
- Creative writing: Fine-tuning better for style
- Simple classification: Prompt engineering sufficient
- Real-time data: May need direct API integration
- Highly specialized domains: Fine-tuning may be better
Implementation Checklist
Before committing to RAG:
- Quantify knowledge base size (>1,000 docs?)
- Measure update frequency (>1x/month?)
- Define success metrics
- Estimate total cost (see Cost Optimization)
- Assess team skills (see Team Structure)
- Plan evaluation strategy (see Evaluation Systems)
- Consider vendor options (see Vendor Evaluation)
Technology Choices: Lessons from Production
Graph Databases vs. SQL
From Production: "In my 10 years of doing data science, I generally stay away from graph modeling. Every time I've seen a company go into this graph-based world, within 4-5 years they decide to move back to a PostgreSQL database."
Why avoid Graph DBs?
- Hiring: Hard to find graph experts; easy to find SQL experts
- Complexity: Most "graph" problems are just 2-3 left joins
- Maintenance: Harder to scale and manage than Postgres
When to use Graph DBs:
- You need to compute 3rd-degree connections very quickly (e.g., LinkedIn social graph)
- You have complex traversals that SQL cannot handle efficiently
Inventory > Algorithms
Insight: "The metadata you have and the inventory you have are much more important than the algorithm itself."
If your recommendation system is failing, don't just tweak the algorithm. Ask:
- Do we have enough data rows?
- Is the metadata accurate?
- Are we missing key inventory?
Example: If "Greek restaurants near me" returns bad results, the solution is usually to add more Greek restaurants to the database, not to change the embedding model.
Common Questions
"Should I use a vector database or just Postgres with pgvector?"
Start with Postgres.
- Simplicity: One database to manage
- Joins: Easy to join vector search with metadata (e.g., "users who bought X")
- Scale: Good enough for <10M vectors
- Switch when: You hit scale limits or need specialized features (e.g., Qdrant/Weaviate for 100M+ vectors)
"How do I handle research papers and new methods?"
Be pragmatic.
- Ignore the hype: Most weekly papers reinvent old ideas
- Focus on data: Solve specific problems in your implementation
- Experiment: Run experiments with your data instead of chasing the latest method
Next Steps
- Vendor Evaluation - Choose the right tools and providers
- Team Structure - Build the right team
- Getting Started - Technical implementation guide