Founder Email Optimizer (DSPy)

Give us your 3 best cold emails and a leads CSV. Get back optimized, founder‑style outreach.

Quickstart

pip install -r requirements.txt
echo 'OPENAI_API_KEY=sk-...' > .env
python cli.py --wins wins.csv --leads leads.csv --template template.txt --out out.csv --optimizer bootstrap

Inputs

• wins.csv: columns subject, body (more rows welcome)
• leads.csv: columns: first_name, company (+ optional persona, pain, product, product_value, customer_example, impact_metric, sender_name, sender_role)

Output

• out.csv: adds optimized_subject, optimized_body, score_0_10, feedback

Notes

Uses DSPy Signatures + Predict modules and BootstrapFewShot or MIPROv2 for optimization.

Default LM: openai/gpt-4o-mini. Override with env:

MODEL_ID=openai/gpt-4o-mini
API_BASE=http://localhost:7501/v1
OPENAI_API_KEY=...

Risk checks & blind spots (called out)

• Correlation to real replies: the LLM judge is a proxy. Expect it to roughly enforce style/shape, not perfectly predict replies. Do a small A/B (1–2 weeks) to calibrate + auto‑tune rubric weights.
• 3 winners is sparse: we compensate by bootstrapping demos + optimizing instructions. If they can provide 5–10, MIPROv2 tends to stabilize.
• Hallucinated facts: we keep slots explicit and penalize fluff; still add a simple regex whitelist for product names if needed.
• Privacy/PII: CSVs likely contain names; don't ship logs outside your network.

Fast extensions

• --variants 3 --pick best (generate N and keep highest judge score)
• Clickbait guard: hard stop if subject contains spammy tokens
• Subject‑line policy: enforce presence of a concrete noun + numeric
• Persona presets: --persona startup_founder vs --persona cio_enterprise
• Cache control: set unique rollout_id when you want fresh generations

Usage Examples

# Basic usage with bootstrap optimization
python cli.py

# Use MIPROv2 optimizer (stronger but slower)
python cli.py --optimizer mipro

# No optimization, just run the base pipeline
python cli.py --optimizer none

# Custom files
python cli.py --wins my_winners.csv --leads my_leads.csv --out results.csv