AIDecision Guide
RAG vs Fine-tuning
RAG is best when answers must stay grounded in changing knowledge. Fine-tuning is best for consistent style or repeated tasks when knowledge doesn’t change often.
Quick Verdict
Choose RAG if...
- Your knowledge changes often (policies, docs, products)
- You need citations and source-grounded answers
- You want fast iteration without retraining cycles
- You need access control or content-level permissions
- You want measurable retrieval quality improvements over time
Choose fine-tuning if...
- You need consistent tone, formatting, or style
- You have repeated tasks with stable patterns
- Latency needs are strict and prompts must stay small
- You can curate high-quality training examples
- Your domain knowledge is stable or encapsulated in examples
Side-by-Side Comparison
Feature
RAG
Fine-tuning
Best for
Grounded answers from docs
Style/behavior consistency
Freshness
High (re-index docs)
Lower (re-train required)
Citations
Natural fit
Not inherent
Data requirements
Docs + queries
Curated examples
Iteration speed
Fast
Slower
Maintenance
Index + retrieval tuning
Training + evaluation cycles
Risk profile
Lower with grounding
Can memorize/overfit if mis-scoped
Cost drivers
Embeddings + retrieval + LLM calls
Training + inference costs
Decision Checklist
Ask yourself these questions to guide your decision:
1Do answers need to reference your latest docs or data?
2Do you need citations/source links for trust?
3Is your core requirement style/format rather than knowledge?
4Do you have enough curated examples for training?
5Will permissions vary by user/team?
6Is time-to-market more important than perfect optimization?
7Do you need to update behavior weekly/monthly?
8What’s your tolerance for retraining cycles and evaluation overhead?
Tradeoffs & Gotchas
RAG requires good ingestion and retrieval tuning to be reliable
Fine-tuning requires curated data and careful evals to avoid regressions
RAG can be more expensive if retrieval isn’t tuned and prompts are large
Fine-tuning can reduce prompt size but doesn’t automatically ground facts
RAG supports freshness by re-indexing; fine-tuning needs retraining
Both approaches benefit from eval sets and observability
Hybrid approaches are common (RAG + light fine-tuning for style)
Security and PII handling must be designed in either way
Our Recommendation
Pick RAG when answers must stay grounded in company knowledge
Pick fine-tuning for consistent style or repeated classification tasks
Use RAG + guardrails when user trust and citations matter
Use fine-tuning when you have high-quality examples and stable tasks
Consider a hybrid: RAG for knowledge, tuning for formatting/style
Frequently Asked Questions
Can RAG replace fine-tuning completely?
Not always. RAG is great for grounding and freshness. Fine-tuning can still help for consistent tone or repeated structured tasks.
Is fine-tuning required for a chatbot?
Usually no. Most production chatbots use RAG + good prompts + guardrails. Fine-tuning is optional depending on your needs.
Which is faster to ship?
RAG is usually faster because you can index documents and iterate without retraining cycles.
Can you add evaluation to both approaches?
Yes. We build eval sets and regression checks so quality improves without surprises.
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