> For the complete documentation index, see [llms.txt](https://kmanu225.gitbook.io/cs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://kmanu225.gitbook.io/cs/ai/taking-a-step-back-notes-on-ai-fluency.md).

# Taking a Step Back: Notes on AI Fluency

There is no shortage of AI news. Every week brings a new model, a new benchmark, a new controversy. The AI Fluency course by [Anthropic](https://anthropic.skilljar.com/ai-fluency-framework-foundations) gave me an excuse to stop and think. I can only recommend following it as I did; find link to the course below.

***

### Three ways to use AI

* **Automation** : tedious tasks you already know how to do. You hand them off.
* **Augmentation** : you and the AI work together, often producing something neither would alone.
* **Agency** : the AI works independently on your behalf.

Recognizing which mode fits a given task is itself a skill worth developing.

***

### The 4D Framework

#### Delegation : share the work intelligently

Decide what AI should do and what you should do. This requires genuine domain expertise: you cannot delegate what you do not understand. Also know your tools, different models have different strengths.

#### Description : communicate clearly

Specify the goal, the method, the constraints, and the expected format. Vague input produces vague output. Treat prompt writing as thinking, not an afterthought.

#### Discernment : evaluate critically

Is the output correct? Is the approach sound? Can you verify the claims? Whether truth is strictly required depends on the task, a brainstorm and a technical report have different accuracy thresholds.

#### Diligence : be responsible

Is your input trustworthy? Is the AI reliable for this task? Are you aware of potential biases? When significant, write a diligence statement covering: which AI you used, how it contributed, your review process, and your assertion of responsibility for the final output.

***

### Know the limitations

Models have a knowledge cutoff, can hallucinate, carry biases from training data, and produce non-deterministic outputs. Without tools, they have no access to real-time information.

The key mindset shift: **learn to grade AI output, not just generate it.** Your domain knowledge is what defines what "correct" looks like.

> For a broader map of the AI landscape, this [mind map](https://app.xmind.com/share/g1RHpBBq?xid=DLYuquX3) is worth exploring.

***

### Daily practice routine

| When             | Action                                                                                    |
| ---------------- | ----------------------------------------------------------------------------------------- |
| Before each task | Split the work: what is mechanical, what needs your judgment, what do you tackle together |
| Each prompt      | Include role, goal, constraints, and expected format. Build a personal prompt library     |
| Each output      | Ask: is this correct? Can I verify it? Do not act on output you have not evaluated        |
| Weekly           | Apply the full 4D loop to one recurring task. Repetition builds fluency, not reading      |
| Weekly           | Test a new model or tool and compare outputs critically                                   |
| Monthly          | Review your prompt library. What worked, what did not, and why                            |

## Ressources

* [AI Fluency: Framework & Foundations](https://anthropic.skilljar.com/ai-fluency-framework-foundations)
* [AI Domains](https://app.xmind.com/g1RHpBBq?xid=DLYuquX3)


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