Use RAG
Choose retrieval when answers depend on changing private or public sources and users need citations, permissions, freshness, and coverage evidence.
Enterprise AI architecture decision
Should an enterprise use RAG, fine-tuning, or AI agents?
These approaches solve different constraints. RAG supplies current, traceable context; fine-tuning changes repeatable model behaviour; tools and workflows execute bounded operations; an agent coordinates decisions across tools under explicit authority.
By Onyx Devs Lab · Published · Updated
KnowledgeRetrieve current evidence
BehaviourAdapt repeatable response patterns
ActionUse tools under bounded authority
Match the method to the missing capability
Begin with the required output and failure consequence, not an architecture label.
Use fine-tuning
Consider tuning when representative examples can improve stable style, classification, extraction, or task behaviour that prompting alone cannot meet reliably.
Use tools or agents
Use deterministic workflows for known sequences; use an agent only when bounded runtime decisions across tools add value and can be evaluated and controlled.
Do not hide one problem inside another technique
Each method introduces its own evidence and operating requirements.
RAG is not guaranteed truth
Measure ingestion coverage, retrieval recall, ranking, citation correctness, answer grounding, permission filtering, and stale-source behaviour.
Fine-tuning is not a knowledge database
Training data becomes harder to update and cite; retain evaluation sets, dataset provenance, versioning, privacy review, and rollback.
Agents are not unrestricted automation
Define tools, identity, authority tiers, confirmation, budgets, idempotency, audit fields, recovery, and human takeover.
How to select the smallest sufficient method
Define the task and evidence
Specify the decision, output, source of truth, freshness, citation need, acceptable errors, and whether any action follows.
Test the simplest approach
Establish a prompt-only baseline before adding retrieval, tuning, orchestration, or autonomous decisions.
Add one missing capability
Introduce RAG for knowledge, tuning for behaviour, or tools for action, then compare against the same evaluation set.
Operate the combined system
Version every component, monitor failures and cost, preserve traceability, and keep an explicit rollback and human path.
Frequently asked questions
Can a system use all three?
Yes. A system may retrieve evidence, use a tuned model for a narrow behaviour, and let an agent coordinate tools. Each component still needs a distinct reason and evaluation.
Does RAG eliminate hallucination?
No. It can improve access to relevant evidence, but retrieval, citation, interpretation, and answer generation can each fail.
When is an agent unnecessary?
When the sequence and rules are known, a deterministic workflow is usually easier to test, control, and recover.
Should proprietary documents be used for fine-tuning?
Only after a documented privacy, rights, retention, security, provider, and deletion review. Retrieval may be more controllable for frequently changing material.
Start with one concrete operating problem.
We will first clarify the workflow, evidence, data boundary, and outcome standard before recommending the next step.
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