Start with the question your application must answer
AI training data and RAG data can begin with the same documents, but they serve different jobs. Training uses data to change a model through a learning process. Retrieval-augmented generation, or RAG, finds relevant information and adds it to the model's input when a question arrives. A document in a retrieval index has not, just by being indexed, trained the language model.
Separate training from scratch from fine-tuning, too. Pretraining builds a model from an initial training process; fine-tuning further trains an existing model. For a team adding a product assistant to an existing application, the practical choice is often between retrieval and fine-tuning, rather than building a foundation model. AWS documents these as separate options, alongside passing a document directly in the prompt.
Write down the failure you are trying to fix before collecting anything. Does the assistant lack today's product information? Does it have the right information but return an unusable format? Those failures call for different data. Also try clear instructions and a small amount of context first. If that already meets the task, you have no demonstrated reason to start a tuning project.
Compare documents for retrieval with examples for tuning
The training column below focuses on supervised fine-tuning, where labeled examples demonstrate the desired result. Other training methods use different data. This is a collection decision table, not a claim that every model accepts the same file format or that one method always produces better answers.
| Decision | Supervised fine-tuning | RAG |
|---|---|---|
| Main job | Teach a task, style or response pattern | Supply relevant information when answering a question |
| Useful starting material | Reviewed inputs with the outputs you want | Documents or records that contain the answer |
| How data is used | Examples enter a training process that adjusts model weights or trainable adapters | Selected content is retrieved and supplied as input to the model |
| When facts change | Review affected examples and whether another tuning run is needed | Update the source and retrieval store, then check what is actually returned |
| First checks to plan | Does the model handle unseen inputs and follow the desired output pattern? | Does retrieval find the right evidence, and does the answer use it correctly? |
| Source references | Keep provenance with the examples; tuning alone does not provide a citation mechanism | Keep source identifiers so the application can present and check citations |
RAG can use keyword, vector or hybrid retrieval. A vector database is one implementation choice, rather than the definition of RAG. Retrieval also adds work and can return irrelevant or incomplete material.
A hypothetical product assistant: separate facts from behavior
Suppose your team owns a small product catalog and wants an assistant to answer questions about compatibility and availability. This is an invented planning example, not an IPMusk customer case or a measured deployment. The questions and checks below illustrate a decision; they report no model results.
For 'Does this accessory fit the current model?', collect the relevant specification and compatibility records. Preserve product identifiers and version information so two similar names do not become interchangeable. For stock counts that change throughout the day, consider querying the authoritative catalog service directly. A periodically refreshed document index might already be too old for the answer you need.
For 'Return every answer in our support format', start with a prompt that defines that format. If evaluation still finds recurring behavior problems, reviewed examples could support a fine-tuning experiment. Each example should show the input and the accepted response, including what to say when compatibility information is missing. Copying an entire catalog into a file does not turn it into those examples.
You might eventually combine a tuned response pattern with retrieved specifications. Keep the two evidence trails separate: which source supported the compatibility answer, and which examples taught the response pattern? That distinction makes it easier to investigate whether a mistake came from stale information, retrieval or the model's handling of the task.
Decide what to keep with each record
Before downloading more material, draft a record layout for a small sample. The following fields are a suggested starting point for the hypothetical catalog, not a required schema from a model provider. Choose fields that let another person trace the answer or review an example without guessing where it came from.
- Source identifier: the original URL, document ID or catalog record, plus the product or version it describes.
- Observation time: when you collected the content, with a time zone. Store the source's own update date separately if one is available; collection time does not prove freshness.
- Usable content: the relevant text or fields, with enough context to interpret qualifications and exceptions.
- Refresh rule: what event or schedule should trigger a new check, and how you will remove superseded material.
- For tuning examples: the task input, reviewed target output and review status. Record which source supports factual statements in that output.
Keep collection errors visible. A blank specification, a login page and an unavailable document should not silently become valid product evidence. A successful HTTP response alone does not show that you collected the intended content.
Use a small evaluation set before expanding collection
For the hypothetical assistant, write questions with expected evidence before deciding how many pages to collect. Include a supported product, an old version, a similarly named item and a question the catalog cannot answer. These are suggested test cases, not a universal sample size. Record the behavior you would accept for each, including when the assistant should say it lacks enough information.
For RAG, inspect the retrieved records as well as the final answer. If the compatibility document never reaches the model, adding examples of polite answers will not repair that missing evidence. If retrieval finds the correct passage but the answer drops an exception, inspect how the application supplies and uses context. Citations help you check an answer; their presence does not prove it is correct.
For a tuning experiment, keep evaluation inputs separate from the examples used to train. Check new inputs, missing facts and required output fields. Compare with the original model using the same task and evidence. Do not treat a lower training loss as proof that a product assistant now answers customers correctly. Your acceptance checks must reflect the job the application will actually do.
Plan how a corrected fact reaches the answer
RAG is often useful for changing information, but freshness is work your application must perform. If a product specification changes, the old passage may remain searchable until the retrieval store is updated. Plan a check that asks about the changed fact after refresh and confirms the old version is no longer used for current-product questions.
Fine-tuning data also needs maintenance. Review examples when the task, terminology or accepted response changes. Training data is not necessarily a one-time collection, and a tuned model can still receive current information through retrieval or another application tool. Choose the update process you can verify rather than assuming either approach maintains itself.
Where a proxy fits in data collection
Start with the data source you already control: a catalog export, documents or a suitable API may be enough. You do not need a proxy just because the data will be used for AI. If your workflow fetches web pages and needs a configured proxy connection, that is a separate networking decision from how the resulting data will be used.
IPMusk provides proxy connections and connection-management features. Its Residential documentation covers HTTP and SOCKS5 connections, country and state selection, and rotating or sticky sessions. Your program still has to fetch the intended content, extract fields and check the records. IPMusk is not a data-cleaning, labeling or model-training platform, and changing the connection does not establish dataset quality.
The related guides below cover proxy types and Python connection setup if that step is needed. For the data decision, start by writing a short collection brief: the questions to answer, the documents or examples required, the source of truth and the checks that will tell you whether the sample works. Use that brief to choose the next collection task.
Technical references
- AWS Prescriptive Guidance, Generative AI options for querying custom documents: docs.aws.amazon.com/prescriptive-guidance/latest/retrieval-augmented-generation-options/options.html
- AWS Prescriptive Guidance, Comparing Retrieval Augmented Generation and fine-tuning: docs.aws.amazon.com/prescriptive-guidance/latest/retrieval-augmented-generation-options/rag-vs-fine-tuning.html
- Microsoft Learn, Retrieval augmented generation (RAG) and indexes: learn.microsoft.com/en-us/azure/foundry/concepts/retrieval-augmented-generation?view=foundry-classic
- Google Cloud, About supervised fine-tuning for Gemini models: docs.cloud.google.com/gemini-enterprise-agent-platform/models/tuning/supervised-tuning
Official technical references checked November 4, 2026. The product-assistant scenario and proposed record fields are editorial examples, not benchmark findings.