Why the count depends on the job
Aranv’s answer on how much data to train an LLM: there is no single number. Vendor minimums for fine-tuning a large language model (LLM) run from tens to hundreds of examples, while training from zero has used hundreds of billions of tokens. Coverage and quality matter more than volume.
Treat every vendor number as that vendor’s guidance. OpenAI recommends starting with 50 well-crafted demonstrations, though it is winding that platform down. Amazon Bedrock’s data preparation page sets minimum records per model, and Google Cloud’s supervised tuning page lists maximums, such as 10M text-only examples, with no recommended count.
Coverage decides more than volume. Five hundred examples of your most common ticket type will not teach the model the rare ones. The authors of the 2023 QLoRA paper reported strong results from tuning on a small, high-quality dataset.
Working out your own number
Let the test set tell you
- 01 Lock a test set first Set aside real cases with approved answers before any training begins.
- 02 Start with a small batch Train on a modest, well-reviewed set and score it on those cases.
- 03 Read the failures Group the misses by type. Each type points to examples you still need.
- 04 Add only what helps Grow the set and retrain until scores stop rising on held-out cases.
Stop adding data once new examples stop moving the score. More of the same costs review time and adds little.
LLM training data requirements by route
The question only has an answer once you know your route. These are the four common situations.
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Hosted fine-tuning
Vendors publish floors, not targets. OpenAI’s guide sets a minimum of 10 examples, and Amazon Bedrock lists 32 or 100 records for some models. A floor only means a job will run.
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Tuning a small model
A small language model (SLM) with one narrow job needs examples that cover each label or format. Rare labels need their own examples, however large the total set is.
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A larger domain model
Many tasks in one domain call for a far larger body of text, often in-house documents at scale, plus a separate example set for each task.
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Training from zero
BloombergGPT’s authors report training on 569 billion tokens from a corpus of over 700 billion. Few companies hold that much usable text, which is one reason most adapt a base model.
Fine-tuning dataset size: what to plan for
Plan around the number of distinct behaviors, not a round total. A classifier with a handful of labels needs examples for each one. A drafting task needs examples for each document type. Separate training, validation and test data first, as Microsoft’s fine-tuning documentation advises.
What we do
- Count of labels or formats
- Examples for each rare case
- Separate validation and test sets
- Duplicate and conflict checks
- Room to add after the first run
When you do not have enough
Retrieval and prompts need no training set, so try them first. Then widen your sources to email, call notes and past documents. Specialists can write examples for rare cases, and synthetic examples can fill gaps if a person reviews them.
What we do
- Prompts with worked examples
- Retrieval over current documents
- Expert-written rare cases
- Reviewed synthetic examples
- Real cases kept for testing
How we approach a project
Quality is where most projects lose time. Inconsistent labels teach the model to hedge, and unreviewed outputs teach it your old mistakes. Our LLM training data services produce reviewed pairs from tickets and documents, AI data preparation handles general cleanup, and how to prepare data for AI covers the groundwork.
Short of examples? Do not train yet. Prompts with a few worked examples and retrieval over current documents may do the job with no training set, and your own team may be able to set that up. Once you have enough to test, LLM fine-tuning or Train Your Own AI Model can follow.
How we work
- Coverage before count
- Held-out cases set the target
- Reviewed examples only
How to find your number
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Count what you have
List usable records by type: closed tickets, approved documents, past redlines. Note who may use them for training.
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Split before you train
Set aside test and validation cases first, so they never leak into training and inflate your score.
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Train on a first batch
Have specialists review a first set, train on it and score the result against a simple prompt baseline.
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Grow only where it fails
Add examples for the failure types you saw, retrain and stop when held-out scores stop rising.
How Aranv helps size your data
A count before any training
We review a sample of your records with your specialists and report which case types have enough examples, which are thin and which are missing.
Expert review stays central
Your subject experts approve examples and settle disputes. We prepare the records, mask personal data and track each dataset version.
Scores decide when to stop
We add examples only where held-out failures show a gap, so review time goes to the cases that actually move the result.
An honest answer if too little
If your records cannot support training, we say so and build with prompts and retrieval instead, while you collect examples from daily work.
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How Much Data to Train an LLM FAQs
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What are the minimum training examples vendors ask for?
It varies by vendor and model. OpenAI’s fine-tuning guide states a minimum of 10 examples. Amazon Bedrock’s documentation lists a minimum of 32 records for one model and 100 combined training and validation records for others. These only let a job run.
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Is 100 examples enough to fine-tune a model?
It can be enough for a narrow job with few labels and consistent answers, and it is rarely enough for a broad one. Scores on held-out cases tell you more than any rule of thumb, including a vendor’s starting suggestion.
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Can we train an LLM with very little data?
Not well on its own. With few records, use prompts with worked examples and retrieval. Specialists can write examples for rare cases, and reviewed synthetic examples can fill gaps, but keep real cases for your test set. If scores do not improve, stop.
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How many test examples do we need?
Enough to cover every case type you care about, with more for the ones that are costly when wrong. A very small test set can swing on a handful of answers, so treat small score changes with caution.
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Can we use customer data for training?
Only if your contracts, privacy notices and the applicable law allow it, so check with whoever handles legal and privacy matters first. Mask personal data the model has no use for. When we do this work, your data is used for your model.
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