{"course":{"slug":"fine-tuning","title":"Fine-Tuning, and When Not To","url":"https://addaly.com/learn/fine-tuning","level":"advanced","track":"deep"},"lesson":{"slug":"what-it-changes","title":"What Fine-Tuning Changes, and What It Cannot Add","url":"https://addaly.com/learn/fine-tuning/what-it-changes","position":2,"of":84,"minutes":8,"answer":"Fine-tuning teaches the shape of an answer, not its content; facts belong in the context window.","keyIdea":"Fine-tuning teaches the shape of an answer, not its content; facts belong in the context window."},"body":{"format":"markdown","text":"## What the gradient actually does\n\nFine-tuning shows the model an input and a target output, then nudges every trainable weight in the direction that would have made those target tokens slightly more likely. Repeat a few thousand times. That is the entire mechanism, and everything fine-tuning is good and bad at follows from it.\n\nWhat it moves efficiently is the conditional shape of the output: given an input like this, produce an output like that. Register, length, structure, formatting, when to refuse, when to ask a clarifying question, which of several behaviours the model already has should fire here. These patterns appear in every example, so every gradient step reinforces them. A hundred examples can visibly change tone. Two thousand can lock a JSON schema so hard the model stops emitting prose at all.\n\n## What it does not add: facts\n\nA fact appears in your dataset a handful of times. The base model saw trillions of tokens. To make one fact reliably retrievable you are fighting an enormous prior with a tiny signal, and gradient descent takes the cheaper route: rather than learn the fact, learn the *form* of a confident answer.\n\nThat produces the characteristic failure. The tuned model answers in your house style, with your structure, at your length, and invents the specifics. It is worse than before, because it now sounds like your best expert while being wrong.\n\nEven where memorisation does happen, the knowledge is:\n\n- **uncitable**: there is no source to show the user;\n- **unupdatable**: the policy changes and you retrain;\n- **undeletable**: someone asks you to remove one record and there is no record to remove.\n\nRetrieval has none of those problems, and its failure mode (\"I could not find anything\") is the one you want.\n\n## The hallucination amplifier\n\nHere is the rule that catches most teams. If a training example's answer depends on knowledge the base model does not have, you are training the model to guess confidently.\n\nSay you build 3,000 question-and-answer pairs from your internal wiki and train without showing the model the wiki. Every one of those examples demonstrates the same lesson: when asked about internal matters, produce a specific, confident answer. At inference, asked about something outside the training set, that is exactly what it does.\n\nThe fix is not more data. It is to train the behaviour with the context present (examples that include the retrieved passage and an answer grounded in it) or to include examples where the correct output is that it does not have the information.\n\n## What it can add\n\n- **Mapping your language onto what it knows.** Your jargon, abbreviations, ticket codes, product names. That is a translation task and it is learnable.\n- **Output shape.** Schemas, tags, field order, the exact vocabulary of a controlled label set.\n- **Decision boundaries.** Which of five categories, which team to route to, when to escalate. This is where a small tuned model does best.\n- **Refusal and calibration.** Including teaching it to say it does not know, which is a behaviour rather than a fact.\n- **Narrow reasoning patterns, partly.** Training on worked traces from a stronger model does raise in-domain performance; that is how many small reasoning models are built. But it transfers the pattern, not the capability underneath. The base's ceiling is still the ceiling, and the gains rarely survive far outside the training distribution.\n\n## What it cannot touch at all\n\nContext length. Tool access. Anything about your infrastructure. And behaviour on inputs unlike anything in your data: off-distribution, the tuned model can be worse than the base, which is the subject of a later lesson.\n\n## One sentence to keep\n\nIf answering requires *knowing* something, put it in the context. If answering requires *deciding* something, fine-tuning can teach the decision."},"check":{"question":"You fine-tuned a 7B model on 5,000 question-and-answer pairs written from your internal documentation. It now answers in your house style and invents plausible specifics that are wrong, sounding more authoritative than it did before. What happened?","options":[{"text":"The examples taught the form of a confident answer far more efficiently than they taught the facts, so the model now produces the shape of an expert answer with invented content.","correct":true},{"text":"Two epochs was not enough exposure for the facts to stick; train longer on the same data.","correct":false,"tempting":"More passes do increase memorisation, but they memorise your exact strings and increase forgetting long before the facts become reliably retrievable."},{"text":"The LoRA rank was too low to hold that much knowledge; raise it.","correct":false,"tempting":"Rank genuinely bounds how much the adapter can change, but higher rank buys verbatim memorisation of your sentences rather than knowledge the model can use."},{"text":"The base model is too small; a 70B base would have absorbed the documentation.","correct":false,"tempting":"Bigger bases do absorb more from fine-tuning, but the same failure appears at 70B: the fix is putting the document in the context, not enlarging the container."}]},"prev":{"slug":"honest-answer-first","title":"The Honest Answer First","url":"https://addaly.com/learn/fine-tuning/honest-answer-first"},"next":{"slug":"diagnosing-the-failure","title":"Diagnosing the failure before choosing the fix","url":"https://addaly.com/learn/fine-tuning/diagnosing-the-failure"},"authorship":"Founder and series editor: Dr Mustafa Mun. Written by AI models to a written standard. Exam keys checked blind by a second model: 3,665 of 3,666 agreed. Academic review invited.","review":{"status":"Draft, open for academic review"},"cite":"Addaly (2026) 'What Fine-Tuning Changes, and What It Cannot Add', in Fine-Tuning, and When Not To. Edited by M. Mun. Addaly. Available at: https://addaly.com/learn/fine-tuning/what-it-changes (Accessed: 31 October 2026).","licence":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"reader":{"schema":1,"title":"What Fine-Tuning Changes, and What It Cannot Add","courseTitle":"Fine-Tuning, and When Not To","module":{"index":1,"slug":"deciding-whether-to-fine-tune","title":"Deciding whether to fine-tune at all"},"answer":null,"keyIdea":"Fine-tuning teaches the shape of an answer, not its content; facts belong in the context window.","hero":{"kind":"plate","image":null,"alt":null,"caption":null},"blocks":[{"t":"h2","id":"what-the-gradient-actually-does","runs":[{"text":"What the gradient actually does"}]},{"t":"p","runs":[{"text":"Fine-tuning shows the model an input and a target output, then nudges every trainable weight in the direction that would have made those target tokens slightly more likely. Repeat a few thousand times. That is the entire mechanism, and everything fine-tuning is good and bad at follows from it."}]},{"t":"p","runs":[{"text":"What it moves efficiently is the conditional shape of the output: given an input like this, produce an output like that. Register, length, structure, formatting, when to refuse, when to ask a clarifying question, which of several behaviours the model already has should fire here. These patterns appear in every example, so every gradient step reinforces them. A hundred examples can visibly change tone. Two thousand can lock a JSON schema so hard the model stops emitting prose at all."}]},{"t":"h2","id":"what-it-does-not-add-facts","runs":[{"text":"What it does not add: facts"}]},{"t":"p","runs":[{"text":"A fact appears in your dataset a handful of times. The base model saw trillions of tokens. To make one fact reliably retrievable you are fighting an enormous prior with a tiny signal, and gradient descent takes the cheaper route: rather than learn the fact, learn the "},{"text":"form","i":true},{"text":" of a confident answer."}]},{"t":"p","runs":[{"text":"That produces the characteristic failure. The tuned model answers in your house style, with your structure, at your length, and invents the specifics. It is worse than before, because it now sounds like your best expert while being wrong."}]},{"t":"p","runs":[{"text":"Even where memorisation does happen, the knowledge is:"}]},{"t":"ul","items":[[{"text":"uncitable","b":true},{"text":": there is no source to show the user;"}],[{"text":"unupdatable","b":true},{"text":": the policy changes and you retrain;"}],[{"text":"undeletable","b":true},{"text":": someone asks you to remove one record and there is no record to remove."}]]},{"t":"p","runs":[{"text":"Retrieval has none of those problems, and its failure mode (“I could not find anything”) is the one you want."}]},{"t":"h2","id":"the-hallucination-amplifier","runs":[{"text":"The hallucination amplifier"}]},{"t":"p","runs":[{"text":"Here is the rule that catches most teams. If a training example’s answer depends on knowledge the base model does not have, you are training the model to guess confidently."}]},{"t":"p","runs":[{"text":"Say you build 3,000 question-and-answer pairs from your internal wiki and train without showing the model the wiki. Every one of those examples demonstrates the same lesson: when asked about internal matters, produce a specific, confident answer. At inference, asked about something outside the training set, that is exactly what it does."}]},{"t":"p","runs":[{"text":"The fix is not more data. It is to train the behaviour with the context present (examples that include the retrieved passage and an answer grounded in it) or to include examples where the correct output is that it does not have the information."}]},{"t":"h2","id":"what-it-can-add","runs":[{"text":"What it can add"}]},{"t":"ul","items":[[{"text":"Mapping your language onto what it knows.","b":true},{"text":" Your jargon, abbreviations, ticket codes, product names. That is a translation task and it is learnable."}],[{"text":"Output shape.","b":true},{"text":" Schemas, tags, field order, the exact vocabulary of a controlled label set."}],[{"text":"Decision boundaries.","b":true},{"text":" Which of five categories, which team to route to, when to escalate. This is where a small tuned model does best."}],[{"text":"Refusal and calibration.","b":true},{"text":" Including teaching it to say it does not know, which is a behaviour rather than a fact."}],[{"text":"Narrow reasoning patterns, partly.","b":true},{"text":" Training on worked traces from a stronger model does raise in-domain performance; that is how many small reasoning models are built. But it transfers the pattern, not the capability underneath. The base’s ceiling is still the ceiling, and the gains rarely survive far outside the training distribution."}]]},{"t":"h2","id":"what-it-cannot-touch-at-all","runs":[{"text":"What it cannot touch at all"}]},{"t":"p","runs":[{"text":"Context length. Tool access. Anything about your infrastructure. And behaviour on inputs unlike anything in your data: off-distribution, the tuned model can be worse than the base, which is the subject of a later lesson."}]},{"t":"h2","id":"one-sentence-to-keep","runs":[{"text":"One sentence to keep"}]},{"t":"p","runs":[{"text":"If answering requires "},{"text":"knowing","i":true},{"text":" something, put it in the context. If answering requires "},{"text":"deciding","i":true},{"text":" something, fine-tuning can teach the decision."}]}],"check":{"question":[{"text":"You fine-tuned a 7B model on 5,000 question-and-answer pairs written from your internal documentation. It now answers in your house style and invents plausible specifics that are wrong, sounding more authoritative than it did before. What happened?"}],"options":[{"text":[{"text":"The examples taught the form of a confident answer far more efficiently than they taught the facts, so the model now produces the shape of an expert answer with invented content."}],"correct":true,"tempting":null},{"text":[{"text":"Two epochs was not enough exposure for the facts to stick; train longer on the same data."}],"correct":false,"tempting":[{"text":"More passes do increase memorisation, but they memorise your exact strings and increase forgetting long before the facts become reliably retrievable."}]},{"text":[{"text":"The LoRA rank was too low to hold that much knowledge; raise it."}],"correct":false,"tempting":[{"text":"Rank genuinely bounds how much the adapter can change, but higher rank buys verbatim memorisation of your sentences rather than knowledge the model can use."}]},{"text":[{"text":"The base model is too small; a 70B base would have absorbed the documentation."}],"correct":false,"tempting":[{"text":"Bigger bases do absorb more from fine-tuning, but the same failure appears at 70B: the fix is putting the document in the context, not enlarging the container."}]}],"order":[2,3,0,1],"why":null},"prev":{"slug":"honest-answer-first","title":"The Honest Answer First","minutes":7},"next":{"slug":"diagnosing-the-failure","title":"Diagnosing the failure before choosing the fix","minutes":10}}}