Documentation in AI Development: Keep Decisions Out of Slack

Documentation in AI Development: Keep Decisions Out of Slack

When development accelerates with AI tools, the volume of decisions increases too. Architectural choices, prioritization decisions, design trade-offs, technical debt acceptances — all of these get made faster because AI presents options quickly.

But where do these decisions live?

Most of the time, in a Slack message. Sometimes something was said in a meeting and never written down. Sometimes it stayed in the AI chat window and was never shared with the team. Two months later a new developer asks "why did we do it this way?" The person who knows isn't at the company anymore. Or they are, but they don't remember.

AI accelerated decisions. Where decisions live didn't change. This mismatch leads to knowledge loss.

Why Decision Scatter Happens Faster with AI

In traditional development, important decisions were made slowly. Meetings happened, emails were exchanged, sometimes an RFC document was prepared. This slowness made recording the decision a natural step.

AI removed this slowness. Now the following scenario is very common: Claude was asked in the morning, three options arrived in five minutes, one was chosen, implementation started. The work was done before noon.

That five-minute evaluation — which alternatives you eliminated and why, which constraints were decisive, which trade-offs you accepted — was never written down anywhere.

The Slack fallacy: "We wrote it in Slack, it's there" is a common belief. But Slack isn't a search engine. Finding that message three months later requires remembering what to search for. If it's not remembered, the decision is lost.

AI chat isn't permanent. Even if Claude or ChatGPT conversations are archived, they aren't shared with the team, aren't associated with the project, and aren't searchable. The rationale AI provided stayed there.

Knowledge tied to a person. Only the person who made the decision at that moment knows why it was made. When that person changes, the knowledge goes with them.

Decision Record Format in Alios

In Alios, every important decision gets written into a node description using a standard format. No separate documentation system needed — it lives next to the task where the decision was implemented, together with its context.

📌 DECISION RECORD — [Title]
Date: [Decision date]
Decision Makers: [Names or roles]
Status: Active / Revised / Superseded

📍 CONTEXT

[Why did this decision become necessary?]
[Which problem or requirement started the discussion?]
[What were the technical and business constraints?]
[If AI tools were used: which tool, what it
suggested — summary]

⚖️ OPTIONS EVALUATED

Option A: [Name]
→ Pro: [Item]
→ Con: [Item]

Option B: [Name]
→ Pro: [Item]
→ Con: [Item]

Why eliminated: [1 sentence for eliminated options]

✅ DECISION MADE

[Which option, one sentence]

Rationale:
[Why this option? Which constraints were decisive?]
[What was taken from the AI suggestion, what changed?]
[What trade-offs were accepted?]

⚠️ ACCEPTED CONSEQUENCES

→ [What was gained]
→ [What was lost or what risk was accepted]
→ [Still unknown — will become clear over time]

🔄 REVISION CONDITION

[Under what condition should this decision be reviewed?]
[Example: If user count exceeds 50K]

🔗 CONNECTIONS

→ Related node: [Task or epic name]
→ AI conversation summary: [Key rationale if applicable]
→ Document: [RFC, benchmark, official source]
→ PR / Commit: [Code where decision was implemented]

Which Decisions Should Be Recorded?

Not every technical choice needs a decision record. Decisions that meet these criteria get written:

Costly to reverse: Database selection, framework decision, API architecture, authentication approach.

AI suggestion was decisive: If AI's comparison or rationale shaped the decision, that process should be recorded.

Created team debate: Decisions with disagreement, decisions where "why not this one?" will be asked.

Constrains other decisions: If one choice narrows the space for subsequent choices, that constraint should be visible.

Small implementation details, tool configurations, and temporary workarounds don't need decision records.

Search and Linking: The Single Source Approach

Writing a decision record isn't enough — it needs to be findable. Two mechanisms in Alios ensure this.

Findability Through Search

When decision record node titles follow a consistent format, search results become meaningful:

DECISION — Database: PostgreSQL vs MongoDB
DECISION — Auth: JWT vs Session
DECISION — Real-time: WebSocket vs SSE
DECISION — Deploy: Monolith vs Microservice

When searching with the "DECISION" prefix, all decision records appear together. A new team member during onboarding can look at this list and understand why the system was built the way it was.

Context Chain Through Linking

A decision record doesn't live alone — it lives alongside the relevant nodes. Each decision record links to the task where it was implemented, and that task links back to the decision record:

📁 Q3 — Real-time Dashboard Feature
│
├── 📌 DECISION — Real-time: WebSocket vs SSE
│   → Decision made: SSE
│   → Rationale: One-directional communication,
│     team can learn quickly with HTTP knowledge
│
├── 📌 SSE infrastructure setup
│   → Linked decision: "DECISION — Real-time:
│     WebSocket vs SSE"
│
└── 📌 Real-time dashboard component
    → Linked decision: "DECISION — Real-time:
      WebSocket vs SSE"

When this chain is built, the developer looking at the task sees the decision, and looking at the decision sees the task. The question "why did we use SSE?" gets answered by looking at the task.

Moving AI Conversations into Decision Records

AI conversations aren't permanent. Links can break, conversations can be deleted, context can be lost. Moving the decisive AI output into the decision record eliminates this risk.

The transfer method isn't copy-paste — it's a summary:

AI conversation summary:
Claude was asked about "WebSocket vs SSE,
one-directional real-time, Node.js + Express
environment." Response summary: "For one-directional
communication, SSE is less complex than WebSocket
and serves the same purpose. Connection limits
should be monitored in HTTP/2 environments."
This rationale had a decisive effect on the choice.

This summary doesn't replace the AI conversation but provides enough context to understand why the decision was made.

Building a Decision Record Culture

No matter how well the system is designed, if writing decision records doesn't become a habit, it doesn't get used. Three practices build this culture.

As a retrospective note at sprint end: At every sprint close, the question "what important decisions were made this sprint?" gets asked and unrecorded decisions get captured. Not all of them — the decisive ones.

As a reference during onboarding: When a new team member joins, the list of nodes with the "DECISION" prefix gets shared. The question "why was it built this way?" gets largely answered by this list.

When a "why?" question arrives, a record gets opened: When someone asks "why did we do it this way?", instead of the answer being written into Slack, it gets written into a decision record node. Written once, findable forever.

Final Thought

AI accelerated decision-making. That speed is valuable. But speed isn't a reason to skip documentation.

The decision record format in Alios builds this balance. A decision gets made, written into a node in five minutes, linked to related tasks. Not a Slack message — a record that lives in the archive.

Six months later the question "why did we do it this way?" arrives. The answer is written in the node.