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