A Great Place to Upskill
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A Great Place to Upskill
Get the latest updates from Product Space
What Every PM Must Know About Training Data
PMs already live in data, but training data is different. Learn how to source, label, and govern the data that actually shapes your AI product's behaviour
This foundation module rewires how PMs think about the word "data." It draws a sharp line between analytics data, which you read to make decisions, and training data, which a model copies to produce behaviour.
Why training data is fundamentally different from analytics data
The full seven-stage data lifecycle and where PM judgment matters most
How to mine your own product for training signals, including the edit-capture loop
Where PMs actually sit in the data pipeline: not writing Python, but defining what "correct" means
This module decides whether the project actually works. It covers the practical economics and mechanics of data labeling, then defines what good data actually looks like across six dimensions.
The five types of labeling tasks and when to use each
The economics of crowd vs. managed vs. expert annotation
How to write a labeling guideline that prevents annotator disagreement
The six dimensions of data quality, none of which is volume
How much data you actually need, anchored to OpenAI's own numbers and Meta's LIMA paper
Most teams choose fine-tuning, RAG, or prompting based on preference and then look for data to justify it. This module reverses the order and frames the decision around one question: is your model failing on facts, or on form?
A data-first framework for choosing between prompting, RAG, and fine-tuning
What the research actually says about when RAG outperforms fine-tuning
When your data should determine the model choice, not the other way around
A worked B2B payments case study showing how a four-day data audit changed the entire architecture decision
This is the execution module. It gives PMs the vocabulary and the practical artefacts to work effectively with data science and ML teams without bluffing and without being sidelined.
Twelve ML terms to know precisely enough to follow any standup
A clear framework for which decisions belong to the PM and which belong to the ML team
How to write a Data Requirements Doc with ten sections an engineer can actually start from
The Data Readiness Review question list that surfaces project-ending surprises before they become project-ending
Akhil Yash Tiwari
Founder
