How to Write Good Technical Articles

Note:

This is the third out of a three part series exploring the usage of AI to communicate better.

• Part 1 talks about why using your voice instead of typing can enhance authenticity.
• Part 2 describes a process for writing with AI while preserving authenticity.
• This article is a general guide on writing good technical articles.
• Additionally, this article is an example of a technical article written using the principles described here. Open the article on the side while reading this one for reference.

1. Why This Article Exists

This article exists because we have a growing gap between execution and understanding.

Inside Divami, projects are moving faster, teams are scaling, and timelines are tightening. In this environment, it is no longer sufficient to make something work once. Concepts are reused across teams, revisited months later, and expected to survive new constraints. When systems break or tech debt accumulates, the root cause is rarely the absence of a tool. It is almost always a concept that was applied without being fully understood.

Before articles like this, the default behavior was simple: solve the immediate problem, move on, and rely on memory or tribal knowledge when the concept resurfaced. This worked when teams were small and timelines were forgiving. It fails at scale. Knowledge decays, context disappears, and each team ends up relearning the same lessons—often repeating the same mistakes. In the cases where an article was written, it was very often shallow: a summary of concepts without the hard-earned understanding that made those concepts work in practice.

Outside the organization, the problem is now compounded by AI. It is easy to generate confident, fluent technical prose without having gone through the failures that produce real understanding. This creates a dangerous illusion of mastery. Readers trust authority, and AI makes it cheap to fake. Without guardrails, technical writing turns into noise amplification rather than learning.

The motivation for this article is therefore explicit and practical. Divami needs technical articles that act as durable knowledge artifacts. They must capture why a concept was explored, what broke before it was adopted, and how it should and should not be used going forward. These articles are meant to be opened during onboarding, referenced during design reviews, revisited during incidents, and reused when similar problems appear again.

This section sets the real-world context—both inside and outside the company—for why such a writing standard is necessary. Everything that follows builds on this premise: technical writing is not about explaining concepts in isolation, but about preserving hard-earned understanding so it can be reused responsibly.

2. Scope, Boundaries, and Prerequisites

Before deciding what to write, understanding how to write is even more essential. In an era where AI can effortlessly generate text that amounts to little more than undirected LLM output, the risk of producing hollow content is high. The real skill lies in using AI as an accelerator without sacrificing intent or authenticity. To address this, I have outlined a distinct AI-assisted writing process applicable to any subject, not just technical work. Read that article before proceeding to this technical one.

Before going any further, this article needs to clearly state what it is not about.

This is not a guide on writing prose, improving grammar, or making articles sound polished. It is not a checklist for SEO, engagement, or distribution. It is not a shortcut for producing content faster. Those problems are orthogonal. This article is concerned only with one thing: how to write technical articles that preserve real understanding and make that understanding reusable inside the organization.

Because of that, there are explicit boundaries.

This article assumes the reader already operates in a technical environment. You are expected to be comfortable reading code, reasoning about systems, and following multi-step technical arguments. If an article needs to pause repeatedly to explain what a variable is or what a function does, it is not a technical article in the sense discussed here.

Expected Prerequisites

The reader is expected to have the following baseline fluency:

• Programming literacy
  Ability to read and reason about code in at least one mainstream language without line-by-line explanation.

• Systems thinking
  Comfort with inputs, outputs, constraints, and failure modes rather than isolated snippets.

• Debugging experience
  Firsthand experience of something breaking in non-obvious ways and the patience required to trace why.

• Basic mathematical or logical reasoning
  Enough fluency to follow invariants, guarantees, and tradeoffs without needing full formal proofs.

This article will not re-teach these skills. It builds on them.

Mental Model for the Rest of the Article

Every technical article that is written using the principles described in this document is treated as a bounded system.

There is always:

• A motivating problem rooted in real, observed constraints
• A clear statement of what existed before this concept and why it failed
• A clearly defined conceptual core and its non-negotiable invariants
• Explicit inputs, outputs, and transformation boundaries
• Frozen terminology and conventions used consistently throughout the article
• Known failure modes, edge cases, and non-happy paths
• Counter‑examples that look valid but are incorrect
• Demonstration of execution on paper using the simplest possible case
• Explicit pause-and-verify checkpoints to test understanding
• Demonstration of execution in real-world scenarios via GitHub repos, Jupyter notebooks, or other reproducible artifacts
• Signals and diagnostics that indicate correct or incorrect behavior during execution
• Tradeoffs and costs: performance, complexity, maintenance, and misuse risk
• Clear criteria for when the concept should not be used
• A point at which understanding is considered sufficient and further depth has diminishing returns

If these boundaries are not established early, readers remain confused even if the explanation is technically correct. Flow diagrams, mental maps, and simplified representations are not decorative. They exist to make the shape of the concept visible before its details are introduced.

This section exists to prevent false expectations. The mental model described here does not refer to examples inside this article, but to any future technical article authored using this framework. If these boundaries or prerequisites are not made explicit in an article, the reader will struggle regardless of how accurate the explanation is.

3. Terminology, Nomenclature, and Conventions

Before any deep explanation begins, terminology must be frozen.

One of the fastest ways to destroy a reader’s understanding is to let words drift. Using the same term to mean different things, or using different terms to mean the same thing, creates silent confusion that compounds as the article progresses. This section exists to prevent that failure mode.

Terminology Freezing

Every technical article must explicitly define its core terms once and then use them consistently.

For each key term:

• State what it means in the context of this article
• State what it does not mean
• Acknowledge common aliases used in literature or libraries, but do not switch between them

Once a term is defined, it is treated as immutable. If a new nuance is introduced later, it must be named as a new term rather than overloading an existing one.

Nomenclature Discipline

Names are not cosmetic. They encode mental models.

Variable names, function names, diagram labels, and section headers should reflect the role a thing plays in the system, not how it happens to be implemented. If the same concept appears in diagrams, math, and code, it must carry the same name across all representations unless there is a strong reason not to.

If a mismatch exists between academic terminology and industry usage, the article must choose one and justify the choice explicitly.

Conventions Used in This Article

To reduce cognitive overhead, conventions should be declared upfront.

Examples:

• How inputs and outputs are represented
• How examples are labeled and referenced
• How assumptions, invariants, and guarantees are marked
• How pauses, checkpoints, and warnings are visually distinguished

These conventions are not stylistic preferences. They are tools to help the reader scan, re-enter, and verify understanding months later without rereading the entire article.

Why This Section Matters

Terminology discipline is a prerequisite for depth. Without it, even a correct explanation becomes fragile. With it, the reader can reason precisely, challenge assumptions, and transfer understanding to new contexts without ambiguity.

Everything that follows assumes that the language of the article is now stable.

4. The Heart of the Heart: Core Concept, Guarantees, and Invariants

This is the non-negotiable core of a technical article.

Everything before this section prepares the reader to think correctly. Everything after this section assumes the reader does think correctly. If this section is weak, the article fails regardless of how polished the rest may be.

What This Section Is Responsible For

The goal here is not coverage. It is correctness.

This section must answer, with precision:

• What the concept fundamentally is
• What problem it is guaranteed to solve
• Under what assumptions those guarantees hold
• What properties remain true regardless of implementation details

If the reader forgets everything else, they should still retain the invariant mental model established here.

Guarantees and Non-Negotiable Invariants

Every serious technical concept has truths that do not bend.

These may be mathematical guarantees, logical constraints, or structural properties. They must be stated explicitly and without qualification. If a guarantee only holds under certain assumptions, those assumptions must be stated alongside it, not implied.

Examples of invariant framing:

• What must always increase, decrease, or remain constant
• What relationships can never be violated
• What transformations preserve correctness
• What failure modes are impossible if assumptions hold

If a concept has no clear invariants, it is either being explained at the wrong level or is not yet understood by the author.

Demonstration on Paper

Before touching real-world code, the concept must be executed in its simplest possible form.

This means:

• Artificially small inputs
• No abstractions
• No libraries
• No performance concerns
• No convenience shortcuts

The purpose is to eliminate every distraction except the concept itself. The reader should be able to manually step through the example and predict the outcome before seeing it.

If the concept cannot be demonstrated on paper, it cannot be trusted in production.

Pause-and-Verify Checkpoints

This section must contain explicit stop points.

At these checkpoints, the reader should be able to:

• Predict the next step before it is shown
• Explain why a transformation is valid
• Identify which invariant is being preserved
• Detect when an incorrect step would violate a guarantee

These pauses are intentional friction. They convert passive reading into active verification.

Counter-Examples and Boundary Breaks

At least one example must be included that looks plausible but is wrong.

This example should:

• Respect most surface-level rules
• Fail due to a violated invariant
• Clearly demonstrate why intuition alone is insufficient

Counter-examples train discernment. Without them, readers learn replication, not understanding.

What This Section Is Not

This section is not:

• An API walkthrough
• A performance discussion
• A collection of edge cases
• A survey of alternatives

Those belong later. This section exists to lock in the conceptual spine of the article.

If the reader cannot articulate the concept, its guarantees, and its invariants after this section, the article has not done its job.

5. From Theory to Practice: Real‑World Demonstration

This section is where theory is intentionally contaminated by reality.

After the core concept, guarantees, and invariants are locked in, the article must demonstrate how those truths survive contact with real systems, real data, real libraries, and real constraints. This is not about completeness. It is about fidelity.

Purpose of This Section

The responsibility here is to prove that:

• The author can translate theory into execution
• The invariants defined earlier still hold
• Any deviation from theory is intentional and understood

If the theory collapses when implementation details appear, then the theory was never properly understood.

Choice of Implementation Medium

Real‑world demonstrations must be reproducible.

Acceptable forms include:

• A GitHub repository
• A Jupyter notebook
• A minimal runnable project
• Any artifact that can be executed end‑to‑end by another engineer

Screenshots, pseudocode, or partial snippets are insufficient on their own. The reader must be able to run something and observe behavior.

What to Show and What to Omit

This section should highlight only the signal, not the noise.

Include:

• The minimal setup required to make the concept work
• The exact locations where the core concept is applied
• Key lines that correspond directly to invariants defined earlier
• Outputs or artifacts that confirm correct behavior

Omit:

• Boilerplate
• Environment setup unless it is conceptually relevant
• Peripheral tooling that does not affect correctness

If a line of code does not serve understanding, it does not belong in the article.

Mapping Back to Invariants

Every practical demonstration must explicitly map back to theory.

The article should point out:

• Which invariant is being exercised
• Which assumption is being relied upon
• What would break if that assumption were violated

This is where the reader learns how to reason about correctness while reading unfamiliar codebases.

Signals and Diagnostics

The reader must be told what to look for.

This includes:

• Expected outputs or intermediate states
• Metrics, logs, plots, or traces that indicate correctness
• Clear signs of failure or misconfiguration

Without these signals, readers can execute code and still walk away with false confidence.

Why This Section Matters

Most technical articles stop at explanation. This section forces accountability.

It proves that the author has executed the concept themselves, understood where theory bends, and can guide the reader through the same path. Without this section, the article remains speculative, regardless of how elegant the theory may be.

6. Failure Modes, Edge Cases, and the Debugging Narrative

This section exists to prove that the author has seen the concept fail.

A technical article that only shows success teaches replication, not understanding. Real mastery is demonstrated by knowing where things break, how they break, and how to recognize that breakage early.

Why Failure Deserves Its Own Section

Most production issues do not arise from unknown concepts. They arise from concepts applied slightly outside their valid boundary. This section makes those boundaries explicit by walking through failure, not avoiding it.

If this section is missing, the article is incomplete.

Common Failure Modes and Edge Cases

This subsection must enumerate the ways the concept fails in practice.

Include:

• Edge cases that violate assumptions
• Inputs that look valid but trigger incorrect behavior
• Scale, data, or environment-related breakdowns
• Situations where the concept technically works but produces misleading results

Each failure mode should be tied back to a violated invariant or assumption introduced earlier.

Debugging Narrative

This is where personal experience matters.

The author should describe:

• What they initially expected to happen
• What actually happened
• Why the failure was confusing at first
• Which signals or diagnostics revealed the real issue
• What correction restored the invariant

This narrative should be honest and specific. Generic statements like “it didn’t work as expected” are insufficient. The goal is to let the reader borrow the author’s scars instead of earning them again.

Distinguishing Bugs from Misuse

Not every failure is a bug.

This section must clearly separate:

• Implementation bugs
• Misconfiguration
• Conceptual misuse
• Fundamental limitations of the technique

Readers must learn to diagnose why something failed before attempting to fix it.

Early Warning Signs

Good practitioners recognize failure before it becomes catastrophic.

List:

• Subtle symptoms that indicate incorrect usage
• Metrics or outputs that should raise suspicion
• Patterns that reliably precede larger failures

These signals train intuition and shorten debugging cycles.

Why This Section Matters

This section builds trust.

It shows that the author did not arrive at understanding by reading documentation alone, but by confronting reality, making mistakes, and correcting them. It also gives the reader a reusable mental checklist for diagnosing failures when they encounter the concept again in a different codebase, dataset, or scale regime. Without this section, the article risks presenting a fragile, idealized version of the concept that collapses under real-world pressure.

7. Variations, Alternatives, and Tradeoffs

Once the reader understands the core concept and has seen it succeed and fail, the scope must widen.

This section exists to prevent tunnel vision. Mastery is not knowing one technique deeply in isolation. It is knowing how it compares, where it fits, and when it should be replaced.

Variations of the Same Concept

Many technical concepts admit multiple variations.

These variations may differ by:

• Assumptions they relax or tighten
• Performance characteristics
• Data requirements
• Implementation complexity
• Failure behavior under stress

Each variation should be described only in terms of how it differs from the core. Re-explaining the concept from scratch is unnecessary and harmful. The reader should be able to reason about each variation by mentally “patching” the invariants learned earlier.

Alternatives That Solve Similar Problems

This subsection answers a critical question: what else could I have used instead?

Alternatives should be introduced with discipline:

• What problem they solve better
• What guarantees they sacrifice
• What new failure modes they introduce
• What complexity they hide or expose

The goal is not to be exhaustive. It is to map the local neighborhood of solutions so the reader can navigate tradeoffs consciously.

Tradeoffs and Cost Surfaces

No technique is free.

This section must explicitly discuss costs across multiple dimensions:

• Time and space complexity
• Operational complexity
• Debuggability
• Maintainability
• Risk of misuse by future readers or teammates

Tradeoffs should be framed as surfaces, not single numbers. What is cheap at small scale may become prohibitive later. What is elegant theoretically may be painful operationally.

When Not to Use This Concept

This subsection is mandatory.

List:

• Scenarios where simpler approaches dominate
• Conditions under which guarantees no longer hold
• Signals that indicate the concept is being overused

Being able to say “do not use this” is a stronger indicator of understanding than knowing how to apply it.

Why This Section Matters

Without this section, readers leave with tools but no judgment.

This section upgrades the reader from an implementer to a decision-maker. It teaches them not just how to use a concept, but how to choose responsibly among competing options in real systems.

8. Heuristics, Review Filters, and Misuse Detection

This section exists to convert understanding into judgment.

Knowing a concept is not sufficient. A practitioner must be able to quickly evaluate existing implementations, spot misuse, and decide whether something is fundamentally sound or merely functioning by accident.

Heuristics for Spotting Correct Implementations

These are fast filters, not proofs.

A correct implementation usually exhibits:

• Clear alignment between code structure and conceptual invariants
• Explicit handling of assumptions rather than implicit reliance
• Observable signals that match expected theoretical behavior
• Failure modes that are predictable and explainable

If an implementation works but cannot be explained in these terms, it is fragile by default.

Heuristics for Spotting Incorrect or Fragile Implementations

Misuse has recognizable patterns.

Common red flags include:

• Excessive parameter tuning to “make it work”
• Silent handling or suppression of errors
• Outputs that look reasonable but violate known invariants
• Reliance on undocumented behavior or side effects
• Inability to explain why a specific configuration was chosen

These signals indicate luck, not understanding.

Heart vs Frills

This subsection forces prioritization.

The article should clearly distinguish:

• The heart of the concept: invariants, guarantees, transformations
• The frills: optimizations, convenience abstractions, syntactic sugar

Readers should be trained to preserve the heart even if the frills change, disappear, or are replaced entirely.

Reviewing Someone Else’s Work

A strong technical article enables peer review.

After reading the article, a reader should be able to ask:

• Which invariant is this code relying on?
• What assumption would break this implementation?
• What signal would tell me this is failing?
• Is this concept even appropriate here?

If the article does not equip the reader to ask these questions, it has not achieved its goal.

Why This Section Matters

This section is what prevents knowledge from degrading over time.

It allows teams to detect misuse early, course-correct quickly, and maintain conceptual integrity even as codebases evolve. Without these heuristics, understanding remains personal and ephemeral instead of institutional.

9. Mastery Gates and Exit Criteria

This section exists to eliminate false confidence.

Reading an article is not evidence of understanding. Agreement is not evidence of mastery. The only reliable signal is execution under constraint. This section defines the minimum bar a reader must clear before claiming they have understood the concept discussed in an article.

What a Mastery Gate Is

A mastery gate is a binary checkpoint.

Either the reader can perform a specific task unaided, or they cannot. There is no partial credit and no subjective interpretation. If the gate is not cleared, the reader should explicitly assume that their understanding is incomplete.

Every serious technical article must define at least one such gate.

Properties of a Good Mastery Gate

A valid mastery gate:

• Exercises the core invariant of the concept
• Requires reconstruction, not memorization
• Fails loudly when understanding is shallow
• Can be verified independently by another engineer

If a task can be completed by copy-pasting or following instructions mechanically, it is not a mastery gate.

Examples of Mastery Gates

Depending on the concept, a mastery gate may take different forms:

• Implementing a minimal version of the concept from scratch
• Reproducing a known result under modified constraints
• Explaining and fixing a deliberately broken implementation
• Predicting system behavior before running it and verifying the prediction

The specific task is less important than the property it tests: whether the reader can reason correctly without scaffolding.

Exit Criteria

This section must also define when the reader should stop.

Exit criteria clarify:

• What “understanding enough” looks like
• Which details are essential and which are optional
• When further depth has diminishing returns for most use cases

Without exit criteria, readers either stop too early with false confidence or dig endlessly without payoff.

Why This Section Matters

This section enforces intellectual honesty.

It reframes the article as a learning contract rather than a narrative. The reader leaves knowing exactly what they can and cannot claim to understand, and what work remains if they choose to go deeper.

10. Internal Applicability and Organizational Reuse

This section exists to prevent shelfware.

A technical article at Divami is not written for the internet. It is written to be used inside the company at specific moments: onboarding, design reviews, implementation, debugging, and incident recovery. If the article does not declare where it fits in that lifecycle, it will be ignored and rediscovered too late.

Where This Article Should Be Referenced

Every technical article should explicitly list the internal touchpoints where it is expected to be used.

Examples:

• Onboarding paths for specific roles or teams
• Architecture and design reviews where this concept is a known dependency
• Implementation runbooks where this concept is a step in a workflow
• Incident postmortems where this concept frequently appears as a root cause or mitigation

If a reader cannot answer “when would I reach for this article?” the article has not been integrated into the organization.

Where This Concept Should Not Be Used Internally

This subsection is mandatory.

Declare internal anti-patterns:

• Projects where the concept adds unnecessary complexity
• Teams or contexts where prerequisites are usually missing
• Situations where simpler baselines should be enforced by default

This is how the article prevents misuse by well-intentioned engineers.

Ownership and Update Policy

Durability requires maintenance.

Each article must declare:

• Who is responsible for updating it when assumptions change
• What signals should trigger an update (new library version, new failure mode, new internal use case)
• What “stale” means for this topic

If no owner exists, the article will decay into historical fiction.

Retrieval Hooks

Articles must be easy to find at the moment of need.

Include:

• Keywords and aliases the company actually uses for this concept
• Links to internal projects where it is implemented
• Links to companion notebooks, repos, or runbooks

The goal is not completeness. The goal is that a reader under pressure can find the right artifact in under a minute.

Why This Section Matters

This is the bridge between individual learning and institutional memory.

It is the difference between an article that proves the author learned something and an article that makes the whole company faster, safer, and more consistent over time.

Conclusion

A good technical article is not a story and it is not a dump of information. It is a reusable learning artifact.

If you remove the fluff, the structure is simple:

• Motivate the concept using real constraints
• Freeze terminology and set boundaries
• Explain the core invariants and guarantees
• Prove execution with reproducible artifacts
• Show failure, not just success
• Map alternatives and tradeoffs
• Teach heuristics for review and misuse detection
• End with mastery gates and exit criteria
• Anchor the article inside the organization so it gets reused

If any of these are missing, the article may still read well, but it will not survive real use.

What This Enables

Without this approach, we ship knowledge that decays. Teams repeat mistakes, reinvent solutions, and trust shallow confidence.

With this approach, we build a living internal library where understanding compounds. The reader does not just finish the article with “I get it.” They leave with the ability to execute, verify, debug, review, and decide.

Practitioner Notes

Before using the concept in a real project:

• Restate the invariants in your own words
• Run the smallest paper example without looking at the answer
• Execute the reproducible artifact and confirm the expected signals
• Trigger at least one failure mode intentionally and observe the diagnostics
• Confirm you are not using the concept where a simpler baseline dominates

Mastery Gate

If you cannot complete the mastery gate defined by the article you are reading, assume you have not understood it.

References

Every technical article should end with references and provenance, partitioned as:

• Foundational: original papers, first principles, proofs
• Practical: applied guides and real-world writeups
• Libraries and syntax: API docs, repos, implementation notes
• Comparative and inspirational: adjacent ideas and alternatives

Each reference entry must include a one-line justification explaining what it contributed.

That is the standard. Not for writing. For learning.

References

(Reference)The whole draft, structure, and various sections of this article were generated in a chatgpt conversation, in under 1 hour.