RAG and fine-tuning change different parts of an AI system. Retrieval supplies information at request time. Fine-tuning changes model parameters through additional training. Both can be useful, and either can be the wrong response to the problem in front of you.
Start by identifying the failure. Is the system missing a current fact? Returning the wrong format? Misclassifying a familiar document? Failing to follow a workflow? Those observations point to different interventions.
What retrieval changes
Retrieval is a good candidate when answers depend on a changing collection of documents, source citations or user-specific access. Updating the source and index can change the information available to an answer without training a new language model.
That freshness is an operating responsibility, not an automatic property. A stale index can serve a superseded policy. Retrieval may miss a relevant passage or return a similar but inapplicable one. Generation can still misread good evidence.
For a policy assistant, begin with a representative query set. Check source coverage and document extraction before assuming you need a larger model or a training run. Evaluate the retrieval stage and the final answer separately.
What fine-tuning changes
Fine-tuning adapts a model using examples. It can be a useful experiment for consistent output structure, specialised task behaviour or a repeatable classification pattern. It does not create an inspectable database of current business facts.
Training quality depends on the examples, task definition and evaluation design. Duplicates, inconsistent labels and leakage between training and test sets can make results look better than they are. Hold out examples that represent the work the model will see after deployment.
Parameter-efficient methods such as LoRA train a smaller set of additional parameters while keeping the base weights fixed. The LoRA paper reports results for specific models and tasks. It does not establish a universal cost or accuracy percentage for every deployment.
Hosted providers may offer fine-tuning too. Fine-tuning is not limited to models running on infrastructure you own, and the resulting rights and retention arrangements depend on the provider’s terms and model licence.
Compare the options against the same work
Swipe or scroll horizontally to compare every column.
| Question | Retrieval | Fine-tuning |
|---|---|---|
| Where does task information come from? | External sources selected for the request | Examples used to adapt model behaviour |
| How are changing facts updated? | Update the source and retrieval pipeline | Additional training may be needed if facts were encoded in weights |
| Can a reader inspect a source passage? | Often, if the system preserves source references | Not a general explanation mechanism for an individual output |
| What should be evaluated? | Retrieval relevance, access, freshness and grounded answers | Held-out task quality, regressions and generalisation |
Try a well-specified baseline first. A clearer task, structured output validation or better document parsing may solve the failure without either a complex retrieval pipeline or fine-tuning.
Use both when the evidence supports it
A tuned model can consume retrieved evidence. For example, retrieval might supply the current document while an adapted model produces a task-specific extraction. That combination adds components, so each should have a measurable purpose.
Compare the baseline, retrieval-only, tuning-only and combined approach on the same held-out tasks where feasible. Include incomplete inputs, permission restrictions and out-of-scope questions. Measure review effort, latency and operating cost as well as answer quality.
A model that memorises evaluation examples may pass a narrow test and fail on a new document layout. An answer that looks correct may cite evidence the user should not be allowed to see. Quality and access checks need separate acceptance criteria.
Keep the data decision explicit
Training datasets, retrieval indexes, request logs and evaluation examples are different stores with different lifecycles. Determine who owns each one, who can access it and how deletion or retention requirements are handled.
Pseudonymisation can reduce direct identification but does not necessarily make information anonymous. Do not assume that removing names makes every dataset safe to share or train on. The EDPB’s explanation of pseudonymisation describes why the distinction matters.
Choose the intervention that addresses an observed failure and survives evaluation. Our Knowledge Systems and AI Software Engineering pages explain the surrounding application work.
Written by Raj Dalal, Advisor at Ryzolv.
Back to Insights