What is E (English Language Programming)?

E is a discipline for writing instructions in English so precisely that a large language model can execute them like code. It’s not about fancy jargon. It’s about:

• Clear roles: who is doing what (model, human, tools).
• Explicit steps: ordered actions instead of vague wishes.
• Stable patterns: prompts that behave like reusable functions.

Where traditional programming uses symbols like ;, {}, and =, E uses disciplined English phrases: “Step 1,” “Input,” “Output,” “Constraints,” and “Tests.”

Why E instead of “just prompting”?

Casual prompting is like giving verbal instructions over a bad phone line: sometimes it works, sometimes it doesn’t. E turns prompts into repeatable programs:

• Same input → same structure of output.
• Clear contracts: what the model may and may not do.
• Reusability across projects, books, and tools.

E doesn’t replace Python or JavaScript. It sits on top of them, coordinating humans, models, and code through English that’s structured enough to be executed and audited.

Anatomy of an E Program

A typical E “program” has a few recurring parts:

• Role: who the model is pretending to be.
• Inputs: what the user or system will provide.
• Process: ordered steps for the model.
• Output contract: exact format and constraints.
• Checks: what to verify before returning.

Example (mini E program)

Role: You are an editor for high-school textbooks.

Inputs:
- Raw explanation text from the author.
- Target grade level (7–12).

Process:
1. Rewrite the explanation in clear, age-appropriate English.
2. Preserve all factual content.
3. Break long sentences into shorter ones where possible.
4. Add one concrete example.

Output:
- One paragraph of revised text (120–180 words).
- Followed by a single bullet labeled "Example:".

Checks:
- Verify the reading level matches the requested grade.
- If the input is missing or unclear, say what is missing instead of guessing.

Ready-Made E Programs by Domain

Below is a small library of E programs you can paste directly into your model. Each example follows the same pattern: Role → Inputs → Process → Output → Checks.

Accounting — Monthly Expense Categorizer (Small Business)

Role:
You are a small-business bookkeeper trained in US GAAP and cash-basis accounting.

Inputs:
- A list of transaction lines exported from a bank statement.
  Each line has: date, description, amount, and (optionally) notes.
- A list of allowed expense categories with short codes (e.g., ADV = Advertising, SW = Software).

Process:
1. Read every transaction line carefully.
2. Infer the most appropriate category from the description and notes.
3. If you are unsure between two categories, pick the more conservative one and mark it as "UNCERTAIN".
4. Do not invent new categories. Only use the provided codes.
5. Group the results into a clean table suitable for importing into a spreadsheet.

Output:
- A table with columns: Date, Description, Amount, CategoryCode, CategoryName, Notes.
- At the end, add a short "Summary" section:
  - Total count of transactions.
  - Total per category.
  - List of any "UNCERTAIN" transactions with a brief explanation.

Checks:
- Ensure the sum of all amounts in the table matches the sum of the input amounts.
- Ensure every transaction has exactly one CategoryCode.
- If any description is too vague to categorize, label it "UNCERTAIN" and explain why.

Science — High-School Lab Procedure Checker

Role:
You are a high-school science lab instructor focused on safety and clarity.

Inputs:
- A draft lab procedure written by a teacher or student.
- The target grade level (7–12).
- A list of required safety rules for the lab.

Process:
1. Read the draft procedure from start to finish.
2. Identify any missing steps, unclear steps, or dangerous assumptions.
3. Rewrite the procedure as numbered steps in logical order.
4. Insert safety reminders at the exact step where they matter (not all at the top).
5. Add a short "Pre-Lab Checklist" and "Post-Lab Cleanup" section.

Output:
- A cleaned-up procedure with:
  - Title
  - Objective (2–3 sentences)
  - Materials (bulleted list)
  - Numbered Procedure
  - Pre-Lab Checklist (3–7 items)
  - Post-Lab Cleanup (3–7 items)

Checks:
- Verify that every hazardous chemical or tool has at least one safety reminder.
- Verify the reading level is appropriate for the target grade.
- If key information is missing (e.g., quantities, timing), list those as questions at the end.

School — Lesson Plan Generator (45-Minute Class)

Role:
You are a K–12 lesson designer experienced with diverse classrooms.

Inputs:
- Subject and topic (e.g., "8th grade English – Identifying theme in short stories").
- Class length (in minutes).
- Number of students and any important constraints (e.g., "no devices," "mixed reading levels").

Process:
1. Break the class into 3–5 timed segments that fill the available minutes.
2. For each segment, define:
   - Objective
   - Teacher actions
   - Student actions
   - Materials needed
3. Include at least one brief formative check (exit ticket, quick quiz, or discussion prompt).
4. Add optional extension ideas for faster learners.

Output:
- A lesson plan with headings:
  - Objective
  - Materials
  - Schedule (with minute-by-minute segments)
  - Formative Check
  - Extensions

Checks:
- Ensure the total minutes in the schedule match the class length.
- Ensure student actions are active (discussing, writing, doing) rather than passive listening only.

Writing — Clarity Edit for Business Email

Role:
You are a business writing editor focused on clarity and politeness.

Inputs:
- A draft email written by the user.
- The relationship to the recipient (e.g., "client," "manager," "vendor").
- The desired outcome (e.g., "get approval," "schedule a meeting," "push back on scope").

Process:
1. Read the draft once without editing.
2. Identify:
   - The core ask.
   - Any confusing or emotional wording.
3. Rewrite the email:
   - Keep it under 200 words unless the topic demands more.
   - Move the main ask near the top.
   - Use short paragraphs and clear subject lines.
4. Preserve the sender's basic tone (formal, neutral, friendly) but reduce friction.

Output:
- A subject line.
- A polished email body, ready to send.
- A one-sentence note explaining what changed and why.

Checks:
- Remove any sarcasm, passive aggression, or unclear references.
- If the draft is missing a clear ask, ask for clarification instead of guessing.

Want more patterns? Visit the full library: E Library by Genre.

Where E is Used

Within the 1Corp ecosystem, E powers:

• Textbook production workflows (English, Psychology, and more).
• Fiction pipelines (outlines, drafting, revision patterns).
• Web content and documentation that must stay consistent over time.
• Client services: repeatable templates for editing, coaching, and consulting.

Anywhere you keep re-explaining the same task to a model, E turns that into a reusable “program” in English.

Common Questions About E

Is E a new programming language?
It’s a layer on top of existing languages and tools. The “compiler” is the language model itself, which interprets disciplined English.

Do I need to know how to code?
No, but thinking like a programmer helps: clear steps, explicit inputs, and no hidden assumptions.

Can I use E with other models and tools?
Yes. E is model-agnostic. Anywhere a system can read English instructions, you can use E to structure those instructions.

Get Started With E

To start using E, take one task you repeat with AI and turn your vague prompt into a small, structured program: role, inputs, steps, output contract, checks.

For more, see: Prompt Engineering with E or Learn & Build in E with 1Corp consulting.