In brief
- RAG retrieves relevant content and provides it to the model as context.
- Fine-tuning adapts a model's behaviour using training examples.
- APIs provide authoritative live data and enable controlled actions.
- The choice follows the type of information required, not a preferred AI tool.
First determine what the application must know or do
Architecture should follow the task. An application that explains policies needs traceable document sources. One that reports a current delivery status must query the system of record. If thousands of texts must always be classified using the same specialised scheme, an adapted model may become useful.
- Does the information change frequently or remain stable?
- Must an answer show its source?
- Is an explanatory response sufficient, or is an exact value required?
- Will the system only read information or also change it?
- Are enough high-quality examples available for the desired behaviour?
Compare RAG, fine-tuning and APIs directly
The approaches are not interchangeable product options. They solve different parts of an application and can be used together. The table shows which question each approach should answer reliably.
| Criterion | RAG | Fine-tuning | Direct API |
|---|---|---|---|
| Type of knowledge | Unstructured documents and approved knowledge collections | Recurring behaviour, format or classification patterns learned from examples | Structured records and functions in a system of record |
| Freshness | Updated by re-indexing sources | Changes require new training data, training and evaluation | Can return the value supplied by the system of record at query time, together with its timestamp and documented cache semantics |
| Source evidence | Supporting passages can be displayed directly | Learned output normally has no traceable supporting passage | The value or record comes from a named business system |
| Actions | Primarily supplies context; changes use a separate tool | Changes model behaviour but does not safely execute transactions by itself | Can read or write in a controlled way when permissions and business rules pass |
| Typical effort | Source maintenance, chunking, permissions, retrieval and answer evals | High-quality training data, training, versioning and regression tests | Interface design, authorisation, validation, error handling and operations |
| Example | Explain a repair instruction with a citation | Classify incident reports consistently using a company-specific scheme | Retrieve current stock or prepare a controlled change |
Use RAG for current, traceable document knowledge
With RAG, the application first retrieves relevant passages from approved sources and provides them to the model together with the question. Knowledge remains outside the model and can be updated or filtered by permission. RAG is particularly useful for policies, manuals, product documentation and internal knowledge collections.
- Content changes and must remain updateable without retraining a model.
- Answers should display sources or supporting passages.
- Knowledge is mainly stored in unstructured documents.
- Different users are allowed to access different content.
- The application should recognise questions that lack supporting evidence.
Use fine-tuning for recurring desired behaviour
Fine-tuning adapts an existing model to a task using many high-quality input and output examples. It can improve consistency for a format, classification scheme or specialised writing behaviour. It is not, however, a reliable replacement for a continuously updated knowledge source.
- The task occurs frequently and can be learned from clear examples.
- Prompting does not achieve sufficient consistency despite evaluation.
- The desired output format or behaviour is relatively stable.
- Representative training data and separate test examples are available.
- Training, evaluation, versioning and future retuning are justified.
Use direct APIs for exact data and actions
If an application needs an account balance, stock level, order status or price, it should query the responsible business system through a defined API. The model can understand a natural-language request or explain the result, but the authoritative value comes from the system of record. Changes should likewise occur only through narrowly scoped and validated functions.
- Live values or complete structured records are required.
- The business system remains the authoritative source.
- Inputs, permissions and permitted actions require technical validation.
- Transactions require confirmation, logging or approval.
- Error responses and missing data must not be replaced by invented values.
Combine the approaches and test the whole system
Enterprise applications often need more than one approach. An assistant can interpret a question, retrieve the relevant policy through RAG and query the current case through an API. A fine-tuned model can additionally perform a narrowly defined classification task. Each component and the complete workflow must be evaluated.
- Step 1
Separate information types
Identify document knowledge, live data, desired behaviour and actions.
- Step 2
Choose the simplest approach
Begin with prompting, search or an existing API.
- Step 3
Add components deliberately
Use RAG for sources, fine-tuning for proven behavioural gaps and APIs for system data.
- Step 4
Test components
Measure retrieval, model answers, data access and permissions separately.
- Step 5
Evaluate end to end
Include real cases, errors, missing data and prohibited actions.
Example: Technical support assistant
A support employee asks how to resolve an error and whether a replacement part is available. RAG finds the approved repair instruction and displays the supporting passage. An API queries the current stock and delivery date in the ERP system. If thousands of incoming fault reports must also be classified according to a stable company-specific scheme and prompting is insufficient, a fine-tuned model can handle that separate task. No single technology needs to perform all three jobs.
What to remember
Use RAG for traceable document knowledge, APIs for authoritative live data and actions, and fine-tuning only when a measured behavioural gap can be addressed with high-quality examples.
Sources and further reading
These primary sources provide further detail on definitions, technical foundations or responsible use.
Content reviewed