Training vs Inference: Two Different Processes
Conversations are not learning; parameters are frozen; billing is per Token — essentials every AI PM must know
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is the key idea behind “Training vs Inference: Two Different Processes”?
Conversations are not learning; parameters are frozen; billing is per Token — essentials every AI PM must know
Inspect what the model is being shown. The practical move is to separate instructions, source material, history, tools, and output rules. Once the context is visible, the right fix is usually easier to choose.
Draw the input and output of one small workflow before changing its prompt or model.
Adding more text when the real issue is relevance, ordering, or a missing boundary.
Conversations do not change any weights
How “Comparison” changes an answer
“Conversations are not learning;” shows that a model does not process the “word count” we see. It processes Token pieces. Tokenization affects input length, how much context fits, and how much computation a request consumes.
Length, information, and context are different
As “Conversations are not learning;” grows, separate three questions: how many Tokens the text becomes, which pieces can change the current decision, and whether older material has fallen outside the context window. Removing repetition is often more useful than simply making the window larger.
Keep what can change the decision
Use “Conversations are not learning;” as an A/B test: keep the same question while removing repeated background, compressing format, and trimming irrelevant history. Compare answer quality, latency, and Token count.
From “Comparison” to “Interactive Experiment: Does What You Say Change the Parameters”
“Comparison” grounds the problem in “Think of a student taking an exam: Training = years of preparation, drilling problems, memorizing knowledge → Inference = sitting the exam, parameters frozen, no longer learning anything new Training Adjusting…”. “Interactive Experiment: Does What You Say Change the Parameters” then moves it toward “Send Context Window AI Hello! How can I help you? Model Parameters (Illustration) 🔒 Parameters Completely Frozen Conversations do not change any weights What you say to the AI does not modify any parameters…”. Together, they show that the lesson is not just a conclusion to remember, but a claim with conditions.
Carry the judgment into the next situation
For long text, keep what can change the conclusion before compressing format and history. A larger context is worth its cost only when the added information is useful.
- “Comparison”: Think of a student taking an exam: Training = years of preparation, drilling problems, memorizing knowledge → Inference = sitting the exam, parameters frozen, no longer learning anything new Training Adjusting…
- “Interactive Experiment: Does What You Say Change the Parameters”: Send Context Window AI Hello! How can I help you? Model Parameters (Illustration) 🔒 Parameters Completely Frozen Conversations do not change any weights What you say to the AI does not modify any parameters…
The final “Finish by testing the claim” brings the discussion to “Conversations are not learning”. The useful thing to carry forward is knowing which judgments must be revisited when input, scale, or risk changes.
I turned one judgment from this article into a small experiment I could run today. Knowing what to observe next is more useful than simply remembering the conclusion.
After reading this, I first looked for the conditions behind the idea instead of copying the method into a project. That order made the later trade-offs much clearer.
When this judgment reaches real work, which constraint should be added first? I am curious which step matters most between reading and the first practical attempt.
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