Is your company's archive ready for fine-tuning?

Syntalith proposes a review of material for model customization, alongside assessment of how the task is performed today. The work can produce a reviewed account of usable examples, gaps requiring an expert's input, and the corrections needed. This gives the buyer a basis for choosing the next step: data preparation, a trial with an existing model, or an assessment of customization.

A correct answer may rely on information you cannot see

Suppose a company wants to draft project status notes. Its archive contains a polished update: “Integration work is complete; documentation is awaiting review.” The author used task changes and team messages available at the time, but those materials were not retained with the note. The finished text alone does not show how it was produced.

Someone familiar with the project can help establish whether the information available to the author at that point can be recovered. Today's task status does not automatically reconstruct that situation. If the basis for the note cannot be confirmed, the example needs additional material or exclusion from training for this task.

This illustrates what the company pays for during preparation. Someone must determine whether an answer is correct given the information the model will receive. Exporting conversations and removing empty rows cannot establish that. Business expertise is needed alongside technical work.

What an archive review should establish

First, define the intended task. A model might draft a response using supplied facts, assign a category, or extract information from a document. Each requires different examples of correct work. An archive of approved messages does not automatically support all of them.

The examples also need to reflect current work. Older answers may follow a retired procedure, while much of an export may consist of copies of the same template. Repetition increases the record count without showing new situations. The review should establish which types of cases are represented and where reviewed examples are missing.

Experts may disagree as well. If one accepts a brief answer and another considers it incomplete, the provider needs an agreement on what the model should reproduce. Part of preparation is resolving those differences and correcting the material before it becomes a reference for training.

OpenAI's technical fine-tuning guidance emphasizes correct, representative examples, consistent reviews, the information needed to answer, and separate test material. These principles help assess an archive independently of the eventual model provider.

Establish what an existing model cannot do first

Fine-tuning means further training an existing model on examples of the desired behavior. Whether that work is worthwhile also depends on what needs improvement. Clear instructions and the right information may already produce answers requiring little editing.

A trial with an existing model reveals the errors that remain. If the model lacks current facts, supplying them is the first issue to address. Our article on fine-tuning and RAG for changing product information explores that decision. If the necessary information is present but the model repeatedly performs the task incorrectly, reviewed examples can support an assessment of customization.

No single record count establishes readiness for every project. The buyer needs to know whether the material represents important situations and correct answers, and whether it supports a fair assessment of improvement. “We have a large archive” leaves those questions open.

A review that helps you commission the right work

A useful review might find that project notes are well written but lack the material from which they were produced. The buyer should see an example of that finding, an explanation of what needs to be recovered, and who can confirm that it matches. This helps estimate the work needed to prepare useful source-and-note pairs before commissioning adaptation.

Separate material must be retained for later evaluation and kept out of customization. It lets the team compare the adapted and baseline models on the same new cases, including the corrections needed in both answers. Reproducing answers seen during training does not show how the tool will help with the next case.

When defining the scope, Syntalith needs someone who understands the task and can confirm whether examples are correct. Your team identifies available material and the conditions for using it. The work can start with a limited archive review and a trial of the current model, with preparation and adaptation scope agreed after discussing the findings.

Describe the task you want AI to perform and the examples you have retained. That is enough to begin a conversation about data readiness; you do not need to send the entire archive at the outset. See Syntalith pricing for information about working with us.