Understand · Use · Build · Deploy
Start where you are. Go as deep as you need.
Learn a concept, try a tool on real work or build something yourself. Every path explains what the technology does and the part you still need to own.
You do not have to finish this before you start.
The fastest way to make sense of AI is to point it at real work. Bring a task you actually have to Bit, get a first move and the parts to keep for yourself, then come back for the concept when you want to know why it worked. The path below is the depth, not a gate in front of it.
Choose your way in.
One path, four stages. Walk it top to bottom, or jump to the stage that fits where you are. You do not need to become a developer to use AI well, but if you want to build and deploy, the path stays visible.
Your path · 0 of 11 lessons
- Step 1 · Understand
Get your bearings
Tell what these tools really do, pick a first task that is safe to get wrong, and check a result before you trust it.
- Start with the foundations
Understand what AI can and cannot do, find a safe first task and learn how to check the result.
Open → - What AI can and cannot do
Separate models, products, search and automation so you can choose a proportionate kind of help.
Open → - How to check an AI answer
Match the checking method to the consequence by inspecting sources, calculations and assumptions.
Open →
Rather than read it all, Bring a first task to Bit →
- Start with the foundations
- Step 2 · Use it well
Get more from the same tool
Give AI the context it needs, work with its short working memory, and catch a confident answer that is still wrong.
- How to provide useful context
Learn why useful source material, examples and rules matter as much as the instruction.
Open → - What a context window really is
A context window is the model's working memory for one answer, measured in tokens. See why it forgets the top of a long thread, and when to paste, retrieve or summarise.
Open → - Why asking the same thing twice can be cheaper
Providers can reuse the stable start of a prompt so repeat requests cost less and answer faster. See what a stable prefix is, what breaks it, and how to order a prompt to keep it.
Open → - Good-looking is not the same as correct
A confident answer can still be wrong. An eval is a small, fixed set of pass-or-fail test cases you re-run to catch that. Build the smallest set that would catch your worst failure.
Open →
Rather than read it all, Try this on your own work with Bit →
- How to provide useful context
- Step 3 · Go further
Understand agents
See how an agent differs from chat, how it takes real actions through tools, and where the human checkpoint belongs.
- Understand agents
See how an agent differs from chat, ordinary software and deterministic automation.
Open → - How an AI does things, not just says them
On its own a model only writes text. Tool calling lets it hand your software a precise request to run a real action, then use the result. See what to expose as a tool and what to leave to words.
Open → - An agent is a model using tools in a loop
An agent is not a smarter chatbot. It plans, calls a tool, looks at the result and decides the next step, until a stop condition. See when a loop earns its keep and where the human checkpoint goes.
Open →
Rather than read it all, Talk an agent idea through with Bit →
- Understand agents
- Step 4 · Build
Make something controlled
Build one small, bounded agent with tests and a human stop point, before anything real is ever connected.
- Build something controlled
Create one small Google ADK agent with a bounded tool, clear tests and a human stop point.
Open →
Rather than read it all, Scope a first build with Bit →
- Build something controlled
The Human Bit Weekly
The useful changes, not every launch.
One short issue every Monday. What changed, what it means for your work, and the part that stays yours.