IEEI Framework (IEEI)

What is the IEEI Framework?

IEEI is a four-part prompt structure built around the sequence Inform, Explain, Example, Input. Each layer serves a distinct purpose: the first two establish context and expectations for the model, the third demonstrates desired output quality, and the fourth delivers the actual content to process.

• I — Inform: Establish what the AI should know
• E — Explain: Clarify the task and context in detail
• E — Example: Provide a reference or sample output
• I — Input: Provide the actual content to process

Unlike freeform prompting, IEEI separates concerns so the model never has to guess what role it should play, what the task requires, or what good output looks like. The result is more consistent, on-target responses — especially for tasks you repeat often or need to hand off to others.

The framework is particularly well suited to educational and explanatory content because each of its four steps mirrors how good teachers frame lessons: establish prior knowledge, explain the learning objective, show a worked example, and then practice with new material.

When to Use the IEEI Framework

How to Use the IEEI Framework

1. 1

   Inform — Establish what the AI should know

   Open with the model's role or the knowledge context it needs. This is not a vague instruction like "be helpful" — it is a precise domain or persona statement. For example: "You are a senior technical writer who specialises in developer documentation for SaaS APIs."

2. 2

   Explain — Clarify the task and context in detail

   Describe what you want the model to produce, for whom, in what format, and under what constraints. Cover tone, length, audience level, and any things to avoid. The more specific this section, the less guesswork the model does.

3. 3

   Example — Provide a reference or sample output

   Supply a concrete example of what a good response looks like. This acts as a one-shot demonstration that calibrates vocabulary, depth, structure, and tone without lengthy meta-instructions. The example does not need to be perfect — it just needs to show the right shape.

4. 4

   Input — Provide the actual content to process

   Clearly delimit the material the model should work on — use markers like "INPUT:" or triple backticks to avoid confusion with the instructional sections above. Everything before this was setup; this is the work.

Prompt Examples

INFORM: You are an experienced software educator who writes beginner-friendly
Python tutorials for adult learners switching careers into tech.

EXPLAIN: Write a short tutorial section (150–200 words) that explains the
concept in the INPUT below. Use plain language, avoid jargon unless you
define it immediately, include one analogy, and end with a one-sentence
takeaway the reader should remember.

EXAMPLE:
Concept: Variables
"Think of a variable as a labelled box. You put a value inside the box,
stick a label on the outside, and whenever you need that value later, you
just read the label instead of memorising the contents.

In Python, creating a variable is as simple as writing:
  name = "Alice"

Here, 'name' is the label and "Alice" is what's inside the box. You can
change the contents at any time by assigning a new value.

Takeaway: Variables let you store and reuse data without repeating yourself."

INPUT:
Concept: For loops

INFORM: You are a professional business analyst who specialises in condensing
lengthy corporate reports into executive-ready summaries.

EXPLAIN: Summarise the document provided in the INPUT section. The summary
must be exactly three paragraphs: (1) the core finding or recommendation,
(2) the key supporting evidence, and (3) the suggested next steps. Write
for a C-suite audience with no technical background. Use active voice and
keep each paragraph under 80 words.

EXAMPLE:
Summary of Q2 Market Expansion Report:
The Asia-Pacific market presents the strongest growth opportunity for the
next fiscal year, with projected revenue upside of 34% above current targets.

Three primary factors support this conclusion: rising smartphone penetration
in Tier 2 cities, a favourable regulatory shift effective January, and two
competitor withdrawals that have left a gap in the mid-market segment.

Leadership should authorise a pilot launch in two target cities by Q3,
commission a localisation study within 60 days, and appoint a regional
lead no later than end of this quarter.

INPUT:
[Paste your document here]

Frequently Asked Questions