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# Project Development
Design and build LLM-powered projects from ideation to deployment.
## Task-Model Fit
**LLM-Suited**: Synthesis, subjective judgment, NL output, error-tolerant batches
**LLM-Unsuited**: Precise computation, real-time, perfect accuracy, deterministic output
## Manual Prototype First
Test one example with target model before automation.
## Pipeline Architecture
```
acquire → prepare → process → parse → render
(fetch) (prompt) (LLM) (extract) (output)
```
Stages 1,2,4,5: Deterministic, cheap | Stage 3: Non-deterministic, expensive
## File System as State
```
data/{id}/
├── raw.json # acquire done
├── prompt.md # prepare done
├── response.md # process done
└── parsed.json # parse done
```
```python
def get_stage(id):
if exists(f"{id}/parsed.json"): return "render"
if exists(f"{id}/response.md"): return "parse"
# ... check backwards
```
**Benefits**: Idempotent, resumable, debuggable
## Structured Output
```markdown
## SUMMARY
[Overview]
## KEY_FINDINGS
- Finding 1
## SCORE
[1-5]
```
```python
def parse(response):
return {
"summary": extract_section(response, "SUMMARY"),
"findings": extract_list(response, "KEY_FINDINGS"),
"score": extract_int(response, "SCORE")
}
```
## Cost Estimation
```python
def estimate(items, tokens_per, price_per_1k):
return len(items) * tokens_per / 1000 * price_per_1k * 1.1 # 10% buffer
# 1000 items × 2000 tokens × $0.01/1k = $22
```
## Case Studies
**Karpathy HN**: 930 items, $58, 1hr, 15 workers
**Vercel d0**: 17→2 tools, 80%→100% success, 3.5x faster
## Single vs Multi-Agent
| Factor | Single | Multi |
|--------|--------|-------|
| Context | Fits window | Exceeds |
| Tasks | Sequential | Parallel |
| Tokens | Limited | 15x OK |
## Guidelines
1. Validate manually before automating
2. Use 5-stage pipeline
3. Track state via files
4. Design structured output
5. Estimate costs first
6. Start single, add multi when needed
## Related
- [Context Optimization](./context-optimization.md)
- [Multi-Agent Patterns](./multi-agent-patterns.md)