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 a nonprofit leader 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 thank-you letter to a donor who gave $500 to our after-school program.
Tell AI who to be and what format to produce.
your line: You are our development director. Warm and specific, never corporate.
Give the background AI cannot guess: who, where, what matters.
your line: We tutor middle-schoolers in Cincinnati. This donor has given twice and cares about reading scores.
Paste a real example of what good looks like.
your line: Match the tone of this past letter we were proud of: [paste one letter].
Check the answer against your standard before you use it.
your line: It must name one concrete outcome and never use the word transformational.
Fix the prompt, do not start over.
your line: If it reads generic, add one real detail about a student the gift helped.
Put it together: a donor thank-you letter
Here is all six slots assembled into one prompt for a donor thank-you letter. Copy it, swap in your own details, and watch how much further it gets than a one-line request.
TASK: Draft a 150-word thank-you letter to a donor who gave $500 to our after-school program. ELEVATION: You are our development director. Warm and specific, never corporate. CONTEXT: We tutor middle-schoolers in Cincinnati. This donor has given twice and cares about reading scores. REFERENCES: Match the tone of this past letter we were proud of: [paste one letter]. EVALUATE: It must name one concrete outcome and never use the word transformational. ITERATE: If it reads generic, add one real detail about a student the gift helped.
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
Snap a photo of a student’s handwritten note and ask AI to turn it into a polished, anonymous quote for your newsletter.
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.