Preparing a fine tuning dataset
Build a clean, consistent, legally sound dataset that teaches a model exactly what you intend
A taste of a lesson
I have 3,000 old support replies. Can I just use them all as training data?
Not as they are. Old replies usually contain three problems. Many are near copies of templates, which would teach the model one stock answer, so deduplicate first. They contain customer names, emails and order numbers, which must be masked and spot checked. And they mix styles from different agents and old policies, so filter to replies that match how you want to answer today. Also confirm your terms allow using customer conversations for training. You may end up with a few hundred good examples, which is fine to start. Quick exercise: name two policy changes that would make some old replies wrong today.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Format examples to match the exact template used at inference
- Audit a dataset for consistency, coverage and unintended patterns
- Remove duplicates and personal data and check rights to use the data
- Measure label agreement and improve guidelines before relabelling
- Decide how many examples to start with and document the dataset
Lesson plan
- 1 The dataset as a specification See how a model learns every consistent pattern in its examples, intended or not. Start
- 2 Formats and templates Structure examples so training matches how the model will be prompted. Start
- 3 Coverage and edge cases Make sure the data reflects the real range of inputs, including hard ones. Start
- 4 Cleaning: duplicates and personal data Remove repeated examples and protect personal information. Start
- 5 Rights, consent and label quality Confirm you may use the data and measure how consistent the labels are. Start
- 6 Size, held out sets and data cards Start small, grow with evidence and document the dataset for others. Start
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About this tutor
For beginners who plan to fine tune a model and want the data right, since data quality decides most of the outcome. You will learn how to format examples as input and output pairs or chat conversations that match how the model will be used, keep style and output format consistent, and cover the edge cases that matter, including cases where the right answer is to decline or ask for more information. The course covers deduplication, removing personal data, checking licences and consent, measuring label noise with a second annotator, deciding how many examples to start with, freezing a held out set early and writing a short data card. Exercises use realistic messy examples.
Reviews
4.7
3 ratingsSample
- Leila F.Sample
Deduplication cut my dataset from 2,400 to 900 examples and the tuned model got better, not worse. That surprised me.
- Yuki S.Sample
Adding examples where the right answer is 'I need more information' fixed a model that used to invent order details. Great tip.
- George A.Sample
The second annotator exercise showed my labelling guide was vague. Practical course, though the data card lesson felt a little short.
About the teacher
Fine tuning with judgment: when to do it, how to do it well, and how to know it worked
9 tutors 428 lessons taught Sample
I teach fine tuning and post training: choosing between prompting, retrieval and tuning, building datasets, parameter efficient methods, instruction and preference tuning, and evaluating the result. My background is in applied machine learning projects where the expensive mistake was usually tuning a model before anyone had defined what better meant. That is why I start every topic with the evaluation...
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