Model fine-tuning and RAG pipelines

General-purpose models don't know your products, policies or data. Retrieval-augmented generation (RAG) and fine-tuning close that gap, and SUDO builds both.

RAG lets a model look up the right information from your documents at answer time. Fine-tuning teaches a model a format, tone or task from examples. We help you choose, then build the pipeline and the datasets behind it.

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What we deliver

  1. 308.1RAG pipelines over documents, knowledge bases and product data
  2. 308.2Chunking, embedding and vector search set up for your content
  3. 308.3Training and evaluation datasets built from your data
  4. 308.4Model fine-tuning for domain-specific tasks and formats
  5. 308.5Evaluation to measure accuracy before and after

How it works

  1. 1Assess

    Decide whether retrieval, fine-tuning or both will move accuracy most.

  2. 2Prepare data

    Clean, structure and label the data the system learns from or searches.

  3. 3Build

    Stand up the retrieval pipeline or training run.

  4. 4Evaluate

    Test against real questions and keep improving.

Who it's for

  • Companies whose AI needs to answer from private knowledge
  • Teams whose use case needs a consistent specialised output

Questions

Should we use RAG or fine-tuning?

RAG is usually the first step when a model needs up-to-date facts from your documents. Fine-tuning helps when you need a consistent format, tone or specialised task. Many systems use both.

Can you build our training dataset?

Yes. We prepare, clean and structure datasets from your data for fine-tuning and evaluation.

Related services

Start with Fine-Tuning & RAG.

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