How Does Documenting Decisions Prevent Costly Engineering Mistakes?

Mar 26, 2026

Problem

I kept watching my team make the same mistakes over and over. We’d choose a database, run into performance issues six months later, and someone would ask: “Didn’t we try this approach before?” No one remembered. The rationale was lost. We’d re-litigate the same decisions repeatedly, wasting hours in meetings.

The worst moment came when a senior engineer left. He had architected our authentication system, and we discovered he was the only one who understood why certain choices were made. When we hit a scaling issue, we couldn’t figure out if his design was intentional or accidental. We spent three weeks reverse-engineering our own system.

This is the hidden cost of undocumented decisions: teams repeat mistakes, knowledge walks out the door, and new engineers onboard through months of osmosis instead of minutes of reading.

What Happened?

I started documenting every decision religiously. Not with fancy tools or complex systems—just Google Docs that I immediately shared with all parties involved.

Here’s what my decision log looked like:

Decision Log: March 15 - Database Choice for User Service

Context:
- Current Postgres instance hitting 80% CPU during peak hours
- Need to handle 10x projected growth
- Team familiar with relational databases, limited NoSQL experience

Options Considered:
1. Scale Postgres vertically (bigger instance)
   - Pros: No code changes, team expertise, proven stability
   - Cons: Expensive at scale, single point of failure

2. Add read replicas
   - Pros: Distributes read load, minimal code changes
   - Cons: Write operations still bottleneck, added complexity

3. Migrate to Cassandra
   - Pros: Linear scalability, proven at scale
   - Cons: 6-month migration, new skill set required, eventual consistency

Decision:
Start with read replicas (Option 2). This buys us 6-12 months while
we evaluate if migration to Cassandra is actually needed.

Who Was Involved:
- Sarah (Tech Lead) - recommended read replicas
- Mike (DBA) - confirmed replication setup
- James (Backend) - reviewed connection pooling changes

Next Steps:
- Mike to set up replica by March 22
- James to update connection strings
- Revisit in 6 months (September review)

Follow-up Date:
September 15, 2026

The simple act of writing this down changed everything. Six months later, when someone asked why we didn’t migrate to Cassandra, I could pull up the document and show them: “We chose read replicas to buy time. Let’s check if we actually need Cassandra now.”

Why This Matters

Documenting decisions prevents costly mistakes in ways I didn’t initially expect.

Institutional memory survives turnover. When that senior engineer left, we didn’t lose his reasoning. His architecture decisions were documented in our shared drive. New engineers could read the context instead of asking “Why did we do it this way?” every day for months.

Pattern recognition emerges. After documenting decisions for a year, I noticed patterns in my own decision-making. I saw that I tended to choose the “safe” option when under pressure, even when the “risky” option had better long-term value. This self-awareness improved my judgment.

Rework disappears. Before documentation, teams would propose the same rejected solution every few months. With documented decisions, I could point to: “We evaluated that in March and rejected it for these reasons.” Meetings became productive instead of repetitive.

Accountability improves. When decisions are documented, ownership is clear. No one can claim “I didn’t know” or “We never agreed to that.” The decision, the rationale, and the participants are all recorded.

I learned to distinguish essential complexity from accidental complexity. As one experienced engineer put it: understanding what complexity is irreducible versus what we introduced through our engineering choices is paramount. Documentation reveals which complexity was necessary and which was self-inflicted.

The Templates I Use

I’ve evolved from simple Google Docs to more structured formats. Here are the templates that work.

Architecture Decision Records (ADR)

For significant architectural decisions, I use ADRs stored in version control alongside the code:

# ADR-001: Use PostgreSQL for Primary Data Store

## Status
Accepted

## Context
We need a primary data store for the user service that can handle
- 10 million users within 2 years
- 1000 requests per second at peak
- Complex relational queries for analytics

## Decision
We will use PostgreSQL as our primary data store with read replicas
for scaling read operations.

## Consequences
- Easier: Team expertise, ACID compliance, rich query capabilities
- Harder: Vertical scaling has limits, write operations bottleneck
- Risk: May need to shard or migrate if growth exceeds projections

## Alternatives Considered
1. MySQL - Similar capabilities, team less familiar
2. MongoDB - Would require denormalization, lose relational integrity
3. CockroachDB - Interesting but immature at decision time

## References
- Decision Log from March 15, 2026
- Database benchmark results in /docs/performance/

Pre-Mortem Template

Before starting major initiatives, I run a pre-mortem:

Pre-Mortem: User Authentication Migration

Assume we are 6 months post-launch and this project has FAILED.
What went wrong?

Potential Failures:
1. Data migration corrupted user passwords
2. Third-party auth provider had extended outage
3. Performance degraded under load
4. Users couldn't reset passwords during migration window
5. Compliance audit failed due to logging gaps

For each potential failure:
- How likely? (High/Medium/Low)
- What prevents this today?
- What warning signs would we see?

Action Items:
- [ ] Add password hash verification to migration script
- [ ] Document fallback to old system (how long retained?)
- [ ] Load test with 2x projected traffic
- [ ] Staged rollout: 1% → 10% → 50% → 100%

Post-Mortem Template

After incidents, I document what happened without blame:

Incident Report: March 20 - Authentication Service Outage

Summary:
At 14:32 UTC, the authentication service became unresponsive,
affecting 15% of users for 47 minutes. Root cause was a database
connection pool exhaustion due to a misconfigured timeout.

Timeline:
14:32 - First error logs appear (connection timeout)
14:35 - PagerDuty alerts triggered
14:38 - On-call engineer investigates
14:42 - Identified connection pool exhaustion
14:45 - Increased pool size as emergency fix
14:48 - Added connection timeout configuration
15:19 - Full service restored

Root Cause:
The connection pool was set to 100 connections but the timeout was
configured for 30 seconds. Under load, connections accumulated
faster than they were released, exhausting the pool.

Impact:
- 15% of users affected (approximately 150,000 users)
- 47 minutes of degraded service
- $12,000 in lost transactions (estimated)

What Went Well:
- Monitoring caught the issue within 3 minutes
- On-call engineer diagnosed correctly on first attempt
- Emergency fix restored service quickly

What Could Be Improved:
- No alert for connection pool utilization (we had the metric but no alert)
- Runbook didn't cover this specific scenario
- Connection pool sizing wasn't validated at projected load

Action Items:
- [ ] Add connection pool utilization alert (threshold: 80%)
      Owner: Mike | Due: March 22
- [ ] Update runbook with connection pool troubleshooting
      Owner: Sarah | Due: March 25
- [ ] Load test with 2x projected traffic before next release
      Owner: Team | Due: April 1
- [ ] Review all connection pools across services
      Owner: Mike | Due: April 5

Lessons Learned:
Connection pool sizing should be validated under projected load,
not just current load. We'll add this to our deployment checklist.

I drew inspiration from aviation accident investigations. Reading about air crash investigations is strangely helpful when thinking about pre- and post-mortems. Aviation’s rigorous documentation and investigation practices prevent repeated failures—software engineering should learn from this approach.

Common Mistakes I Made

Over-engineering the documentation system. I initially tried to build a complex decision-tracking system with templates, workflows, and approvals. It was too much friction. Engineers avoided it. Simple Google Docs worked better because they had zero barrier to entry.

Waiting for perfection. I’d think “I’ll document this properly later” and then never get around to it. The solution was to document immediately—shared docs, imperfect format, sent to stakeholders right away. Done is better than perfect.

Documenting only successes. It’s tempting to document only the decisions that worked out. But the failures are more valuable. When I started documenting rejected approaches and failed experiments, the value of our decision log doubled.

Omitting the “why.” Early on, I’d write “Decided: Use Redis for caching.” That’s useless. I needed to document: “Decided: Use Redis for caching because we need sub-millisecond response times and Memcached doesn’t support our data structures.” The rationale matters more than the decision itself.

Making documentation inaccessible. Decision logs buried in email threads or Slack messages get lost. Everything needs to be searchable and centralized. I now use a shared drive with consistent naming conventions.

Treating documentation as a separate task. Documentation became a burden when I treated it as something to do after the decision. I learned to make it part of the decision process: open the doc, write the context, then discuss. The documentation is the decision.

How to Start

If you’re not documenting decisions today, start simple:

Create a shared folder for decision logs
Document immediately after any important call or meeting
Include: context, options considered, decision, rationale, participants
Share with all parties right away—don’t wait for perfection
Revisit the document when the decision is challenged or revisited

As your practice matures, add structure:

ADRs for architectural decisions
Pre-mortems for major initiatives
Post-mortems for all incidents
Quarterly reviews of past decisions

Summary

In this post, I explained how documenting decisions prevents costly engineering mistakes. The key point is that simple documentation practices—context, options considered, decision made, rationale, and participants—transform individual knowledge into organizational wisdom.

Start with shared documents that you send immediately. Add structure like ADRs as the practice matures. Make documentation part of the decision process itself, not a separate administrative burden. And document failures—they’re often more valuable than successes.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Discussion: Documenting Decisions
👨‍💻 Architecture Decision Records
👨‍💻 Pre-mortems and Post-mortems Best Practices

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!