Module 2 Summary: Fluent Is Not the Same as True
Consolidate the module decisions and keep the reusable resource available for later work.
What you can explain now
You can now give a simple answer to this question:
How does a common text model create a response?
The app gathers the current instructions and context. A tokenizer turns the text into tokens. The model chooses a likely next token. The token is added, and the process repeats.
This can create clear and useful language. It does not automatically prove the facts.
Main ideas
Generative AI uses many kinds of content
It can create text, images, audio, video, code, and structured content. Not every kind of model works in the same way.
Tokens are pieces
A token may be a word, part of a word, punctuation, or another piece. One token is not always one word.
Text is built step by step
A common text model predicts the next token again and again. The earlier context affects what comes next.
Training and prompting are different
Training changes model parameters. A prompt usually gives current input to a model that is already trained.
Context has limits
The context may include instructions, chat, files, found passages, and tool results. A context window limits how much can be processed together.
Clear answers can still be wrong
A model may add false names, dates, rules, or citations. It may also leave out an important condition.
The full app adds abilities
Search, retrieval, calculators, saved memory, citations, and actions are not automatic model abilities. The full app may add them.
Agents can act
An agent may choose tools and take several steps towards a goal. More action needs stronger limits and review.
The five-layer explanation card
When you study an AI output, ask:
- Given: What came from the prompt or supplied material?
- Generated: How did the model build the response from tokens and context?
- Added abilities: Did the app use search, tools, memory, or agent actions?
- Support: Which claims are supported, added, false, or missing?
- Next step: What must be checked, removed, corrected, or approved?
Before the knowledge check
The ten questions test your understanding of tokens, generation, context, false claims, and full-app abilities.
You need 8 out of 10. Read the feedback before trying again.
What comes next
You now understand how output is produced and why it can fail.
Module 3 asks a new question:
Is AI the right tool for this task at all?
You will compare AI with search, calculators, fixed rules, other software, and human-led work.
Keep this rule:
A clear answer is still an answer that must be checked.