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 accounting professional 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 200-word client email explaining why their Q3 estimated payment increased.
Tell AI who to be and what format to produce.
your line: You are a client communication specialist at a CPA firm. Plain language, empathetic, no jargon.
Give the background AI cannot guess: who, where, what matters.
your line: The client is a first-year S-corp owner, cash basis, nervous about cash flow and surprised by the increase.
Paste a real example of what good looks like.
your line: Match the tone of this past email they responded well to: [paste one].
Check the answer against your standard before you use it.
your line: It must explain the why, use only the figure I provide, and end with one clear next step. No tax position I have not verified.
Fix the prompt, do not start over.
your line: If it reads defensive or technical, rewrite it to lead with the reason and lower the anxiety.
Put it together: a client email explaining why an estimated tax payment went up
Here is all six slots assembled into one prompt for a client email explaining why an estimated tax payment went up. Copy it, swap in your own details, and watch how much further it gets than a one-line request.
TASK: Draft a 200-word client email explaining why their Q3 estimated payment increased. ELEVATION: You are a client communication specialist at a CPA firm. Plain language, empathetic, no jargon. CONTEXT: The client is a first-year S-corp owner, cash basis, nervous about cash flow and surprised by the increase. REFERENCES: Match the tone of this past email they responded well to: [paste one]. EVALUATE: It must explain the why, use only the figure I provide, and end with one clear next step. No tax position I have not verified. ITERATE: If it reads defensive or technical, rewrite it to lead with the reason and lower the anxiety.
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 stack of paper receipts and ask AI to draft a clean, categorized expense list that you then verify against the actual receipts.
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.