design documentation
**Design documentation** provides **comprehensive written specifications that capture technical decisions, architecture, and implementation plans** — serving as a communication tool for stakeholders, a reference for implementers, and a historical record of why decisions were made, essential for complex ML projects that involve multiple teams and evolve over time.
**Why Design Docs Matter**
- **Alignment**: Ensure stakeholders agree on approach before building.
- **Communication**: Bridge between product vision and implementation.
- **Review**: Enable early feedback when changes are cheap.
- **Documentation**: Create reference for future maintainers.
- **Onboarding**: Help new team members understand systems.
- **Decision Log**: Record why choices were made.
**When to Write Design Docs**
```
Scenario | Need Design Doc?
-----------------------------|------------------
New major feature | Yes
Significant refactoring | Yes
Cross-team integration | Yes
Bug fix | No
Minor enhancement | Probably not
Complex technical decision | Yes
```
**Design Doc Structure**
**Standard Template**:
```markdown
# [Project Name] Design Document
## Overview
[1-2 paragraphs summarizing what this is and why it matters]
## Goals
- Primary goal 1
- Primary goal 2
## Non-Goals
- Explicitly out of scope item 1
- Explicitly out of scope item 2
## Background
[Context needed to understand the problem]
## Design
### System Architecture
[High-level architecture diagram and explanation]
### API Design
[Interface definitions, contracts]
### Data Model
[Schema, relationships, storage decisions]
### Key Components
[Major modules and their responsibilities]
## Alternatives Considered
| Option | Pros | Cons | Decision |
|--------|------|------|----------|
| A | ... | ... | Rejected |
| B | ... | ... | Selected |
## Security Considerations
[Threat model, security measures]
## Privacy Considerations
[Data handling, compliance]
## Testing Strategy
[How this will be tested]
## Rollout Plan
[How this will be deployed]
## Timeline
| Milestone | Date | Owner |
|-----------|------|-------|
| Design approved | YYYY-MM-DD | @author |
| MVP complete | YYYY-MM-DD | @dev |
| Full rollout | YYYY-MM-DD | @team |
## Open Questions
- [ ] Question 1?
- [ ] Question 2?
## References
- [Link to related docs]
- [Link to prior art]
```
**ML/LLM-Specific Sections**
**Model Architecture**:
```markdown
## Model Architecture
### Base Model Selection
- Model: Llama-3.1-8B
- Rationale: Balance of capability and inference cost
### Fine-Tuning Approach
- Method: LoRA (r=16, alpha=32)
- Training data: 50K instruction pairs
- Expected training time: ~4 hours on 1x A100
### Evaluation Metrics
| Metric | Target | Measurement |
|--------|--------|-------------|
| Accuracy on eval set | >85% | Held-out test |
| Latency P95 | <500ms | Load test |
| Cost per 1K queries | <$0.50 | Production monitoring |
```
**RAG Architecture**:
```markdown
## RAG Architecture
### Retrieval Pipeline
1. Query embedding (OpenAI text-embedding-3-small)
2. Vector search (Pinecone, top-k=5)
3. Reranking (optional: Cohere reranker)
4. Context injection (max 4000 tokens)
### Vector Store
- Provider: Pinecone
- Dimensions: 1536
- Index type: Cosine similarity
- Partitioning: By tenant_id
### Chunking Strategy
- Method: Recursive character splitting
- Chunk size: 500 tokens
- Overlap: 50 tokens
### Data Flow Diagram
[ASCII or linked diagram]
```
**Writing Best Practices**
**Be Concise**:
```
❌ "The system will utilize a sophisticated
microservices-based architectural paradigm..."
✅ "The system uses microservices for X, Y, Z."
```
**Lead with Impact**:
```
❌ Background → Goals → Design (readers lose interest)
✅ One-line summary → Impact → Design → Background
```
**Include Diagrams**:
```
Architecture diagrams (boxes and arrows)
Sequence diagrams (interactions over time)
Data flow diagrams (how data moves)
State diagrams (system states and transitions)
Tools: Mermaid, draw.io, Excalidraw
```
**Show Trade-offs**:
```markdown
## Alternatives Considered
### Option A: Use external RAG service
**Pros**: Faster to implement, managed infrastructure
**Cons**: Higher cost at scale, less control
**Decision**: Rejected due to cost at projected volume
### Option B: Build custom RAG pipeline
**Pros**: Full control, lower marginal cost
**Cons**: More engineering effort upfront
**Decision**: Selected
```
**Review Process**
```
1. Draft design doc
2. Self-review (check completeness)
3. Request reviews (stakeholders, experts)
4. Address feedback
5. Approval meeting (for major designs)
6. Final sign-off
7. Begin implementation
8. Update doc as design evolves
```
**Living Documentation**
- Link implementation PRs to design doc.
- Update when design changes significantly.
- Archive completed docs for reference.
- Reference in onboarding materials.
Design documentation is **thinking made visible** — the process of writing forces clarity, the document enables collaboration, and the artifact serves as institutional memory, making design docs essential for building complex systems successfully.