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

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

## SUMMARY
[Overview]

## KEY_FINDINGS
- Finding 1

## SCORE
[1-5]

def parse(response):
    return {
        "summary": extract_section(response, "SUMMARY"),
        "findings": extract_list(response, "KEY_FINDINGS"),
        "score": extract_int(response, "SCORE")
    }

Cost Estimation

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

Validate manually before automating
Use 5-stage pipeline
Track state via files
Design structured output
Estimate costs first
Start single, add multi when needed

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