Custom LLM and Fine Tuning

Most teams ask for fine-tuning when they actually need one of the cheaper two options, so this is the first decision we work through honestly. Prompting is changing what you tell the model, including giving it examples in the prompt; it is free, instant, and solves a surprising amount. Retrieval-augmented generation, or RAG, adds your own knowledge to the model at query time by fetching relevant documents and feeding them in as context. Fine-tuning, supervised fine-tuning specifically, actually adjusts the model's weights by training it on hundreds or thousands of your input-output examples. The rule we live by: if the problem is the model does not know your facts, that is a RAG problem, because facts belong in retrieval where you can update them instantly, not baked into weights you would have to retrain.

Fine-tuning earns its place when the problem is behaviour rather than knowledge: a consistent format, tone or style you cannot reliably get from prompting, a narrow classification task you want to be fast and cheap, or compressing a capability onto a smaller open-weight model like Llama or Mistral to cut inference cost at high volume. The trade-offs are concrete. Prompting and RAG are cheap and updatable but bounded by the base model's behaviour. Fine-tuning bends behaviour and can lower per-call cost, but it needs quality data, costs money to train, and goes stale as your needs change. Our standard path is prompting, then RAG, and only fine-tune with a measured reason, the same framework we apply across our custom AI tool development.

A fine-tuning project is mostly a data project. The first and longest phase is data preparation: assembling input-output pairs that show the model exactly the behaviour you want, cleaning them, removing duplicates and errors, and formatting them consistently. Quality beats quantity here, a few hundred excellent, representative examples routinely outperform thousands of noisy ones, because the model learns whatever patterns are in the data, including the mistakes. Where labels do not exist yet, we define a clear labelling guide and review a sample for consistency before scaling, because inconsistent labels teach the model to be inconsistent.

Training itself is the short, almost mechanical step: we run supervised fine-tuning on a chosen base model, holding back a validation set, and watch for overfitting, where the model memorises the training set instead of generalising. Then comes evaluation against a held-out test set of real cases the model never saw, scored on the metrics that matter to you rather than a generic benchmark. Only once it clears that bar do we deploy, behind the same guardrails and monitoring we use for any production model. We are upfront before we start about whether fine-tuning is even the right call, because the responsible-deployment discipline in our AI development services applies here too.

A domain-specific LLM is a model shaped to one industry or one business: its vocabulary, its document types, its brand voice and the answers its users actually need. We build these for teams where a general model keeps getting the nuance wrong, in legal, medical, financial or deeply technical domains where the right answer depends on specialist context. The build usually combines retrieval over your corpus with light fine-tuning for tone and format, which together produce a model that reads and responds the way your domain expects without you paying to train a model from scratch, something almost no business actually needs to do.

Privacy is the other big driver, and it is where open-weight models earn their keep. When data cannot leave your environment, for regulatory or contractual reasons, we deploy an open-weight model such as Llama or Mistral inside your own cloud account or on-premise, so prompts and your proprietary data never travel to a third-party API. You trade some raw capability against the largest hosted frontier models for full control over where data lives and who can see it, and for many regulated clients that trade is non-negotiable. A private model is also a natural backbone for autonomous workflows, which is exactly what our agentic process automation builds on.

A custom model is not a deliverable you hand over and forget; it needs evaluation and upkeep to stay useful. Evaluation starts with a representative test set, real inputs with known-good outputs, scored on the dimensions you care about: factual accuracy, format adherence, tone, and refusal of out-of-scope questions. For open-ended tasks where there is no single right answer, we use rubric-based scoring and, increasingly, a stronger model as an automated judge against that rubric, validated against human ratings. The point is to have a number you can track, because without one you cannot tell whether a change helped or hurt.

The threat to watch is drift. The world changes, your products and policies change, and the base models you build on are updated by their vendors, so a model that scored well at launch can quietly degrade. We re-run the evaluation suite on a schedule and whenever an underlying model changes, watch production logs for low-confidence and failed cases, and retrain or refresh retrieval when scores slip. Guardrails stay in place throughout to catch unsafe or off-scope outputs. This is the same maintenance loop we wrap around any custom tool, including those in our custom AI tool development, so the model keeps earning its place rather than slowly rotting in production.

Do we have enough data is the question that decides whether fine-tuning is even on the table, and the realistic answer surprises people: less than they fear, but cleaner than they have. For shaping tone, format or a narrow task, a few hundred high-quality, representative examples often move the needle, and a few thousand is plenty for most business tasks. You are not training a model from scratch, you are nudging an already-capable one, so the goal is a focused, consistent dataset rather than a giant noisy one. Many clients already have the raw material sitting in their support tickets, past documents, approved responses and historical decisions; the work is curating and labelling it, not gathering it from nothing.

Quality dominates quantity because the model learns whatever is in the data, including contradictions and errors, so a hundred carefully reviewed examples beat a thousand inconsistent ones. Where you are short, there are honest ways to extend a dataset, careful augmentation and synthetic examples generated and then human-checked, but we are cautious here, because synthetic data that nobody verifies just teaches the model to imitate its own guesses. If after the audit there genuinely is not enough usable data, we say so and steer you to retrieval and prompting instead, which need almost no training data. That build-versus-fine-tune call is one we make openly across our AI development services.