RAGFine-tuningArchitecture
RAG vs fine-tuning vs context caching in 2026: when to use each
Three techniques to make an LLM answer with your information. Which to pick based on case, budget, and volume.
April 5, 2026 · Lixto Labs Team · 1 min read
The classic dilemma
"I want my chatbot to know my business." We hear this on every discovery call. The underlying question is always the same: how do we get our information into the AI? Three paths.
Option 1: RAG (Retrieval-Augmented Generation)
You search relevant info on the fly (vector DB or hybrid search) and inject it as context per query.
- When: changing info (prices, stock, policies, large FAQs), medium-large data volume, source traceability needed.
- Cost: medium. Needs embeddings infra + vector DB.
- Latency: adds 100-300ms.
Option 2: Fine-tuning
Train the model with examples to adjust behavior or knowledge.
- When: very specific tone/format, repetitive tasks with thousands of examples, complex classification.
- Cost: high upfront, low at inference.
- Latency: very low if you run your own model.
- Risk: information gets fossilized. Every business change requires re-training.
Option 3: Context caching
Send a huge context once and providers cache it for follow-up queries at much lower cost.
- When: large but stable corpus (manuals, legal docs, monthly-updated knowledge base).
- Cost: very low when context is reused often.
- Latency: very low.
The reality: it's usually a combo
- Context caching for the "master manual" (policies, branding, top products).
- RAG for dynamic data (inventory, prices, customer orders).
- Fine-tuning only if quality is still insufficient and you have data.
Always start with the simplest option and only escalate when numbers justify it.