The Prompt Architecture: T.E.C.R.E.I.
Six slots that turn a vague ask into a usable result
What you'll be able to do
- Build any prompt from the six T.E.C.R.E.I. slots instead of a blank box
- Recognize which slot is missing when an answer comes back generic
- Extend the same framework to images, audio, video, and code
Why structure beats clever wording
Most people treat prompting like a magic phrase: type the right words and the answer appears. That is the wrong model. A strong prompt is not clever, it is complete. It carries the same six pieces of information a sharp colleague would need before they could help an educator do the work well.
Those six pieces are T.E.C.R.E.I.: Task, Elevation, Context, References, Evaluate, Iterate. This is the prompt architecture. When an answer comes back flat, you do not need a new phrase. You need to find the slot you left empty.
If an earlier module in your track taught a five-part prompt anatomy (role, task, constraints, examples, and an output spec), this is the same idea extended, not a rival method. Evaluate and Iterate are the two steps you run after you send the prompt.
The six slots
Name the exact thing you want, in one sentence.
your line: Draft a 150-word email to a parent about their child missing three homework assignments.
Tell AI who to be and what format to produce.
your line: You are a supportive 7th-grade teacher. Warm, direct, solution-focused.
Give the background AI cannot guess: who, where, what matters.
your line: The student is bright but disengaged this quarter. The parent works nights and reads email on a phone.
Paste a real example of what good looks like.
your line: Match the tone of this past email that landed well: [paste one].
Check the answer against your standard before you use it.
your line: It must offer one concrete next step and never sound accusatory.
Fix the prompt, do not start over.
your line: If it feels cold, add one specific thing the student does well.
Put it together: a parent email about a struggling student
Here is all six slots assembled into one prompt for a parent email about a struggling student. Copy it, swap in your own details, and watch how much further it gets than a one-line request.
TASK: Draft a 150-word email to a parent about their child missing three homework assignments. ELEVATION: You are a supportive 7th-grade teacher. Warm, direct, solution-focused. CONTEXT: The student is bright but disengaged this quarter. The parent works nights and reads email on a phone. REFERENCES: Match the tone of this past email that landed well: [paste one]. EVALUATE: It must offer one concrete next step and never sound accusatory. ITERATE: If it feels cold, add one specific thing the student does well.
Multi-modal prompting: same framework, more inputs
Multi-modal prompting means interacting with AI using various input and output modalities, including text, pictures, audio, video, and code. The core T.E.C.R.E.I. framework still applies. You just take extra care to specify the input and output types and the context around them.
The default. Words in, words out. Everything below still rides on a clear text instruction.
Paste a screenshot, photo, or chart and ask AI to read, describe, critique, or redraw it.
Hand AI a recording to transcribe, summarize, or pull action items from.
Share a clip or its transcript so AI can summarize, timestamp, or repurpose it.
Paste a spreadsheet formula, script, or config and ask AI to explain, fix, or generate it.
In your world
Photograph a page of student work and ask AI to suggest three specific, encouraging feedback comments tied to what it sees.
The rule of thumb: name the modality in both directions. Tell AI what you are giving it (a photo, a recording, a spreadsheet) and what form you want back (a table, three bullet points, a redrawn chart). The clearer the input and output types, the better the result.