Technical Writing & Communication Prep Guide

Technical writing is one of the most-undervalued senior-engineering skills and one increasingly probed in interview formats. Strong written communication compounds across documentation, design docs, code reviews, post-incident reviews, and stakeholder emails. This guide covers technical-writing preparation grounded in the AIEH Communication assessment.

Who this guide is for

Engineers preparing for technical-writing interview rounds. Some employers (Google, formerly Stripe in some formats) include written-exercise components.
Engineers building written-communication skills for daily work and senior-role expectations.
Career-transition candidates demonstrating written-communication portfolio.

Why technical writing matters

Three reasons:

Senior engineering scales through writing. Engineers who can articulate design decisions in writing reach more people than ones who only communicate in conversations. Promotions to staff and principal levels often depend on this scaling pattern.
Asynchronous communication is increasingly default. Remote and distributed teams rely on written communication; weak writing produces real friction.
AI tools amplify good writing. AI-augmented drafting works best when the human provides clear framing and judgment; AI doesn’t substitute for knowing what to say.

Core technical-writing skills

Six skill areas:

Audience awareness. Different audiences ( engineering peers, product managers, executive leadership, customers) need different framing. Strong writers recognize the audience first and adapt accordingly.
Lead with the conclusion. The Pyramid Principle (Minto 1987): start with the answer, support with reasons, ground each reason in evidence. The recommendation-up-front pattern was covered in detail in the comm-architecture-recommendation explainer.
Clear structure. Headings, bullet points where appropriate, paragraph organization. Strong technical writing is scannable; readers should find what they need without reading every word.
Concrete examples. Abstract claims are weaker than examples. “X is more reliable” is weak; “X reduced page-load p99 latency from 800ms to 120ms” is strong.
Editing discipline. First drafts are usually too long. Editing for clarity often means cutting words, not adding them.
Visual aids appropriate to the medium. Diagrams for architectural communication, code blocks for specific examples, screenshots for UI documentation.

Common technical-writing formats

Six formats senior engineers should be fluent in:

Design docs. Proposing a technical design with context, problem statement, proposed solution, alternatives considered, trade-offs articulated. The canonical senior-engineering writing format.
Post-incident reviews. Documenting what happened, why, what we learned, what we’ll change. Blameless framing is the discipline that makes them productive.
Code-review comments. Concrete, constructive, focused on what matters. The discipline of high-leverage feedback.
Stakeholder updates. Status updates, roadmap communications, leadership briefings. Audience-aware, conclusion-first.
Customer-facing documentation. API docs, user guides, troubleshooting. Different from internal documentation in audience and tone.
Email and async messages. The most-frequent written format; scaling discipline for getting messages right matters because they’re so frequent.

Strong design-doc patterns

Design docs are the canonical senior-engineering writing format:

Start with the problem. What are we solving and why? Provides context for the rest.
State the proposed solution clearly. What you’re proposing in 1-2 sentences before the details.
Document alternatives considered. What else you evaluated and why you didn’t pick it. Honest alternatives discussion builds credibility.
Articulate trade-offs explicitly. What this design optimizes for, what it sacrifices, when you’d revisit.
Outline implementation plan. Who, when, dependencies, rough effort estimate.
Surface open questions. What you don’t know yet, what input you need from reviewers.

A design doc that follows this structure produces substantially better stakeholder review than free-form documents.

When AI assistance helps technical writing

Three patterns where AI is valuable:

First drafts. AI can produce reasonable starting drafts that the human refines. The “blank page” problem is real and AI helps solve it.
Editing for clarity. AI is reliable at suggesting cuts and rephrases that improve readability.
Translating across formats. Converting a code comment into a design-doc section, or design-doc language into customer-facing documentation.

Three patterns where AI is less valuable:

Original argumentation. The argument has to come from the writer; AI assistance can produce fluent-but-wrong reasoning.
Domain-specific examples. Specific, true examples from your work make writing strong; AI can fabricate plausible-sounding examples that don’t hold up.
Tone calibration for specific audiences. AI defaults often miss the right tone for specific organizations.

How this maps to AIEH assessments

Communication is weighted in essentially every AIEH role bundle. The full Communication assessment probes written-communication skills directly. See the communication interview prep for the assessment-side preparation; this guide covers the broader skill development.

Resources for deeper study

The Pyramid Principle by Barbara Minto. The canonical reference for structured business writing.
On Writing Well by William Zinsser. General writing-craft book that applies to technical writing.
Style: Toward Clarity and Grace by Joseph Williams. Style guide focused on clarity in expository prose.

Code-review writing specifically

Code-review comments are the most-frequent technical-writing format for many engineers. Patterns that distinguish high-leverage code-review comments:

Lead with the question or recommendation. “Should this handle the case where X?” or “Consider extracting this into a helper” gets read; “I noticed that…” followed by lengthy explanation often gets skipped.
Distinguish must-fix from nit. Strong reviewers signal which comments block merge vs which are optional improvements. Reviewers who treat every comment as must-fix produce review-fatigue and slow merges; reviewers who never block produce weaker code-quality outcomes.
Specific over general. “Here’s a specific case where this would break” produces better outcomes than “this might have edge cases.”
Constructive over critical. Tone matters in async written communication where intent isn’t readily inferred. Strong reviewers frame feedback as collaborative problem-solving rather than judgment.
Acknowledge what’s good. Code review focused entirely on problems misses the chance to reinforce patterns worth repeating. Strong reviewers occasionally call out elegant solutions or careful edge-case handling.

Post-incident review writing

Post-incident reviews are the highest-stakes technical- writing format for many production-oriented engineers:

Blameless framing. Effective post-incident reviews attribute outcomes to systems and decisions rather than individuals. The blameless framing produces honest reporting that compounds organizational learning; blame-framed reviews produce defensive reporting that conceals the actual failure modes.
Timeline reconstruction. Detailed timeline of events with timestamps, actions, signals received, and decisions made. The timeline is the foundation for the rest of the analysis.
Causal analysis vs root-cause-analysis. Modern SRE practice emphasizes causal analysis (multiple contributing causes) over root-cause analysis (single cause). The shift produces better follow-up interventions.
Action items with ownership. Post-incident reviews that don’t produce specific action items with explicit ownership produce learning-without-action; the action items close the feedback loop.

Common technical-writing pitfalls

Three patterns:

Burying the conclusion. Long preambles before the main point. Lead with the conclusion.
Excessive jargon. Audience-aware writing uses jargon when it’s the right precision and avoids it when it’s barrier-to-understanding.
Passive voice ambiguity. “It was decided that” obscures who decided. Active voice surfaces attribution and makes writing clearer.

How AIEH portable credentials interact with technical writing

Communication is weighted in essentially every AIEH role bundle, with technical-writing as a load-bearing sub-skill. Three specific patterns:

Calibrated communication signal. AIEH’s Communication assessment probes written communication directly via the calibrated-quality scenario format; candidates who score well demonstrate the BLUF and steel-manning patterns covered above.
Cross-employer credential portability. Strong technical writers can translate the skill across employers; portable credentials provide validated signal that supports the cross-employer move.
Reduced reliance on portfolio review. When candidates carry portable Communication credentials, hiring loops can rely less heavily on writing-sample portfolio review for baseline assessment, focusing portfolio work on role-specific judgment instead.

Takeaway

Technical writing is a senior-engineering skill that scales impact across documentation, design docs, code reviews, post-incident reviews, and stakeholder communication. Strong technical writing is audience-aware, conclusion- first (BLUF / Pyramid Principle), structurally clear, example-grounded with specific evidence rather than abstract claims, and edited for clarity rather than for length. The specific format-discipline patterns (design- doc structure, code-review tone, post-incident-review blameless framing) compound across an engineering career. AI assistance helps with first drafts and editing for clarity but doesn’t substitute for original argumentation, domain-specific examples, audience-specific tone calibration, or the editorial judgment that distinguishes strong from competent technical writing.

For broader treatment of communication-as-hiring-signal, see the Communication sample, scoring methodology, and the comm-architecture-recommendation explainer that probes the BLUF pattern central to senior-stakeholder written communication.

Sources

Minto, B. (1987). The Pyramid Principle: Logic in Writing and Thinking. Pitman Publishing.
Williams, J. M., & Bizup, J. (2017). Style: Lessons in Clarity and Grace (12th ed.). Pearson.
Zinsser, W. (2006). On Writing Well: The Classic Guide to Writing Nonfiction (30th anniversary ed.). HarperCollins.
US Army. (2014). Army Regulation 25-50: Preparing and Managing Correspondence. Source for the BLUF (Bottom Line Up Front) standard.
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology. Psychological Bulletin, 124(2), 262–274.