Module 3 · Files, tools and your own data Lesson 9 of 15
Speed, small open models and the canvas — Le Chat's own feature
Le Chat answers quickly and edits documents in a canvas; behind it, the same company publishes small models that run on modest hardware. Together they make the case for matching the model to the job instead of always reaching for the biggest one.
The concept
Because most real work is small. Rewriting a paragraph, pulling a table out of a PDF, translating a message: none of it needs a frontier model, and using one is slow and expensive. Matching the model to the job is an engineering judgement, and it is the one this course is really teaching.
Ask for the format first and keep the request short — these models reward brevity. Use the canvas when you are going to edit rather than read. And when the work is repetitive, ask whether a small local model would do it just as well.
Here is the shape of it, in the form you will actually use:
Attached: [60-page tender document]
Extract every requirement that mentions a deadline or a penalty.
Output a table with exactly these columns:
clause | requirement (max 15 words) | deadline | penalty
One row per requirement. No rows for anything without a deadline.
If a page is unreadable, add a row saying which page and stop there.
Nothing outside the table.Common mistakes
The mistake is judging every model by the hardest question you can think of. The right question is which model is good enough for this task, at this volume, at this cost.
Worked example
A short model regenerating clean text on a laptop with no network is fast enough to feel instant, and free.
Your practice task
Take one document, extract a table from it, edit the result in the canvas, and then run the same extraction on a small local model and compare the two.
Do this before moving on. Reading a solution teaches the shape of it; typing it teaches you.
Key takeaway
Because most real work is small.
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