About
A home for practical, field-tested learnings from people building with AI.
What this is
Field Journal is a central place for people in the industry to share “field journals”: practical learnings from shipping AI systems in the real world. Think implementation notes, hard-won lessons, and patterns that survived contact with production.
Our mission
- Make AI work actionable. Share what actually moved the needle, with enough detail to replicate.
- Compress the learning curve. Turn one team’s experience into everyone’s leverage.
- Raise the bar on craft. Celebrate clear thinking, sound evaluation, and good engineering hygiene.
What we publish
- Case studies: what you built, constraints, what worked, what didn’t, what you’d change next time.
- Playbooks: repeatable workflows (prompting, tooling, evaluation, guardrails, deployment, monitoring).
- Postmortems: failures and near-misses—root causes, mitigations, and prevention.
- Notes and experiments: small proofs with clear outcomes and limitations.
Editorial principles
- Clarity over hype. Prefer specific claims to sweeping statements.
- Evidence over vibes. Include examples, numbers, or decision criteria when you can.
- Context matters. State assumptions: data, users, latency, cost, privacy, constraints.
- Respect privacy. Don’t share secrets, customer data, or sensitive prompts.
How to contribute
Email contribute@fieldjournal.ai with your post idea. If it’s a good fit, someone will reach out with next steps.
To protect the information contributors share, authors are anonymized by default. In practice that means posts are published under a pseudonym or as “Anonymous”, even if we coordinate directly with you behind the scenes.
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