Direct AI Effectively
Interacting with AI systems in ways that produce useful and relevant results. Because most AI tools depend heavily on the input they receive, users must learn to provide clear instructions, include necessary context, and iterate strategically. This does not require coding skills, it requires a mental model for framing prompts, sharing information, and improving responses.
"Direct AI Effectively" is the third foundational content area of the DOL AI Literacy Framework (TEN 07-25). It covers how to interact with AI systems to produce useful results: contextual framing, structured prompting, supplying relevant input data, iterating on outputs, and avoiding vague or misleading prompts. No coding skills required.
Last verified against the DOL source: August 20, 2026. What changed
Score your program on these five checkpointsWhat the framework says
Paraphrased: because most AI tools depend heavily on the input they receive, users must learn to "provide clear instructions, include necessary context, and guide the system toward better outcomes." Directing AI well "does not require coding skills but it does require a mental model for how to frame prompts, share information, and iterate strategically to improve the quality of responses."
The five example content areas:
- 3.1
Contextual framing
Providing background information, intended audience, tone, or specific goals helps shape the AI's response to better match the user's needs in different workplace scenarios.
- 3.2
Prompting techniques
Structuring prompts clearly, using step-by-step instructions, and specifying formats or outputs allows workers to unlock more advanced or precise capabilities of the AI system.
- 3.3
Supplying relevant input data
Workers should understand when and how to include the most relevant data, supporting materials, or examples to improve the accuracy and usefulness of AI outputs.
- 3.4
Iterating on outputs
Effective users treat AI interactions as an ongoing process, using follow-up prompts to clarify, refine, or reframe results until they meet the desired standard or purpose.
- 3.5
Avoiding vague or misleading prompts
Workers should recognize how prompt clarity and word choice affect outcomes and adjust their approach accordingly to avoid ambiguity.
Reproduced from the public framework text under 17 U.S.C. section 105. Read the source on dol.gov.
What it means in practice
This is the provision closest to a teachable skill with an immediate payoff, and the framework treats it as craft, not trick. Two markers distinguish real instruction here. First, iteration is taught as the default, not a rescue move: the framework describes effective users treating AI interaction "as an ongoing process," refining "until they meet the desired standard or purpose." Second, input discipline: knowing what context and data to supply, which is also where responsible-use training (Area 5) bites, because the same lesson that teaches "supply relevant data" must teach "never supply confidential data."
How programs fail this provision
Prompt-template training: learners receive a laminated sheet of magic prompts and learn nothing about why structure works, so the skill collapses the moment the task or tool changes. The framework's antidote is explicit, show learners "poorly written examples" and let them watch phrasing, specificity, and structure change the outcome (see Delivery Principle 1). Inspection question: can graduates improve a bad output through follow-up prompts, or do they just re-roll and hope?
The five checkpoints this provision scores on
Learners are taught to frame requests with background information, intended audience, tone, and goals.
Framework marker: Contextual framing
Learners practice structured prompting: clear step-by-step instructions and specified output formats.
Framework marker: Prompting techniques
Learners are taught when and how to supply relevant data, supporting materials, or examples to improve AI outputs.
Framework marker: Supplying relevant input data
Learners practice iterating, using follow-up prompts to clarify, refine, or reframe until outputs meet the standard.
Framework marker: Iterating on outputs
Learners are shown how vague or misleading prompts degrade outcomes, and how to adjust wording to remove ambiguity.
Framework marker: Avoiding vague or misleading prompts
How GAGE addresses it
In AI Literacy and Professional Conduct, this provision sits in Practical AI Workflow Design and Prompt Engineering and Critical Thinking and Context Engineering. Every topic in the program ends in a mastery assessment, and the teach-back gate asks the learner to explain the material back in their own words before it counts as understood, which is the difference between a program that covers a checkpoint and one that can evidence it.
Module titles come from the program registry, so this cannot drift away from what the program contains. GAGE is a private company: the program is built to the DOL framework, and DOL neither certifies nor endorses it or any other program.
Questions about this provision
Is prompt engineering a separate skill from AI literacy?
Under the DOL framework, foundational prompting is *part of literacy* (Area 3); advanced prompt engineering is a specialization the framework places on the continued-learning pathway (Delivery Principle 5).
Does the framework endorse specific prompting frameworks?
No. It names capabilities, framing, structure, input data, iteration, clarity, without endorsing any branded methodology.
Does your program actually meet this provision?
Answer the five checkpoints above and the other 55, and get a dated report that scores your program provision by provision. Free, and your answers stay in your browser.
What this means for workers, job seekers, and students
TEN 07-25 for Workers: What the DOL AI Literacy Framework Means for Your Career
GAGE (Global Academy of Generative-AI Education) is a private education company, not affiliated with or endorsed by the U.S. Department of Labor. DOL does not certify or endorse training programs. Framework summaries are drawn from the public TEN 07-25 document. Read TEN 07-25 on dol.gov. Last verified: August 20, 2026.
Last verified against the DOL source: August 20, 2026. What changed