RAG Systems Explained: When to Use RAG vs Fine-Tuning for Business AI
A pragmatic guide to RAG vs fine-tuning for business AI: decision criteria, architecture, cost/latency trade-offs, a comparison table, and production checklists.
Why RAG (and why most chatbots fail)
Most “AI chatbots” fail because they:
- answer without grounding (hallucinations)
- lack access to your docs / tickets / database
- can’t cite sources or explain the reasoning path
- break under real-world constraints (latency, cost, privacy, access control)
RAG (Retrieval-Augmented Generation) solves this by retrieving relevant, up-to-date context and generating answers grounded in your data.
If you’re evaluating what to build, start here: Services (or Contact if you want to discuss your case).
The minimal RAG architecture
At a high level:
- Ingest: pull documents from sources (Drive, Confluence, Notion, DB exports, PDFs)
- Chunk: split into retrievable units
- Embed + index: store vectors + metadata
- Retrieve: semantic + keyword/hybrid search
- Re-rank: improve relevance (cross-encoder or LLM-based)
- Generate: answer with citations + guardrails
Chunking: the highest-leverage decision
Chunking is a product decision as much as a technical one.
Good chunking rules of thumb:
- chunk by structure (headings, sections, Q/A), not by raw character count
- keep metadata (doc title, section path, timestamps, ACL)
- store source URLs so you can cite and deep-link
Retrieval patterns that hold up in production
Hybrid search
Use a blend of:
- BM25 / keyword search (great for exact phrases, IDs)
- vector search (great for meaning)
Re-ranking
Re-ranking is how you turn “okay” retrieval into “trustworthy” retrieval.
Guardrails: make answers safer and more useful
- Citations: show which sources were used
- Refusal mode: “I don’t know based on available docs”
- Follow-up questions: ask for missing context
- Access control: enforce per-user permissions at retrieval time
Evaluation (how to know it works)
Measure:
- retrieval quality (recall@k, MRR)
- answer correctness (graded tests)
- citation accuracy
- latency + cost
Next steps
- If your backend is Laravel, see: Laravel AI Integration (RAG + Agents)
- If you’re exploring agent orchestration, see: AI Agent Frameworks (2026)
Want a fast, pragmatic assessment of your data + use case? Let’s talk.
Want this implemented end-to-end?
If you want a production-grade RAG assistant or agentic workflow— with proper evaluation, access control, and observability—let's scope it.