RAG, Fine-Tuning and Tool Use: Which Problem Does Each Solve?

Evidence check: 25 October 2026. Product details reflect the referenced documentation observed at this cutoff; exact plans, prices, regional eligibility and unverified release dates are not claimed. This is an editorial guide, not a hands-on test.

Start with the missing capability

Retrieval-augmented generation (RAG) addresses missing or frequently changing facts. A search system finds relevant excerpts and supplies them to the model at answer time. Fine-tuning changes learned response tendencies or specialized behavior during model training; it is not a substitute for a frequently updated, access-controlled document store. Tool use addresses the ability to query live systems, compute, or take action. An agent can combine all three, but the combination is not automatically superior to a simple retrieval pipeline.

A knowledge-base example

A company wants staff to ask about its current travel policy. Index approved policy documents with version numbers, owners, effective dates and access-control metadata. Retrieve passages for the actual question, filter to the user’s allowed documents, and produce an answer that identifies the policy version. The model should say when sources conflict or the necessary document is missing. Uploading a PDF is not proof that the correct paragraph will be found; verify retrieval independently.

When an external tool is necessary

“What is the policy?” is a retrieval question. “How many unused days does my account show?” requires authorized access to live data. “Submit my request” is a write operation requiring separate approval. A design that blends these into one all-powerful agent makes least-privilege controls harder. Keep retrieval read-only, distinguish citations from instructions inside retrieved text and require explicit action contracts for writes.

Evaluate components independently

Measure whether the correct document enters the retrieved candidate set before judging answer quality. Then evaluate citation faithfulness, refusal when evidence is absent, access-control leakage, freshness, end-to-end latency and token consumption. Fine-tuning experiments must compare against a baseline on held-out examples. A compelling demonstration based on a handful of curated questions is not a measured improvement.

Practical architecture

Use document ingestion → parsing → metadata and permissions → indexing → retrieval → answer generation → groundedness checks. Add tool invocation only where live state is needed. Refresh or revoke indexed material when source permissions or versions change; deletion from a source system must be reflected in your index. Prefer deterministic filtering before generative synthesis.

Decision rule

Use RAG for updatable proprietary knowledge, fine-tuning for repeatable behavioral specialization supported by evaluation, and tools for live reads/writes. If a workflow can be assembled from ordinary functions, an autonomous planner may not be necessary.

Worked architecture comparison

Consider an employee who asks, “What is our travel reimbursement rule, and has my claim been paid?” The policy portion belongs to permission-aware retrieval of an approved policy version. Payment status requires a narrowly authorized live account tool. Fine-tuning might improve classification or writing conventions, but it does not retrieve the latest paid/unpaid state. A system might combine all three, yet combining them should be justified by measured error reduction rather than feature completeness.

Measure each layer separately

For retrieval, score whether the correct document and effective version were returned, including cases where the document is missing. For generation, check whether the answer faithfully attributes policy and avoids invented exceptions. For tools, verify account isolation, parameter authorization and exact external state. For adaptation, maintain a held-out dataset and compare against prompting and retrieval baselines; do not evaluate on examples copied into the training corpus. Include deletion tests so revoked policies cannot continue surfacing from a stale index.