RUN RESEARCH SPIKES WITH AN "AI INTERN": REAL LESSONS FROM PAIRING WITH CHATGPT
A startup used ChatGPT as an "AI intern" to run a month-long data spike and scaffold a cross-branch query API. In a real experiment, a startup paired with Chat...
A startup used ChatGPT as an "AI intern" to run a month-long data spike and scaffold a cross-branch query API.
In a real experiment, a startup paired with ChatGPT to handle a month-long intern-style spike: design and scaffold a cross-branch query API. The write-up details setup, prompts, guardrails, and what "done" looked like.
Agents did well at scaffolding, glue code, search, and docs; they struggled with ambiguous specs, latent data quirks, and evaluation. The emerging pattern: run agents as interns behind tests and reviews, wire tight data-access sandboxes, and measure time-to-first-query and defect rate versus a human baseline.
Agent-led spikes can cut time to explore risky ideas while humans own specs, reviews, and evaluation.
Turning spikes into measured workflows reduces rework when promoting prototypes to production.
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Recreate the AI-intern spike: have ChatGPT scaffold a read-only query API on a branch snapshot; track time-to-first-success and defect rate versus a human.
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Build an eval harness: contract tests, schema drift checks, and cost/latency logging for agent-suggested changes.
Legacy codebase integration strategies...
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Pilot agents in a dev-only mirror with scrubbed data and least-privilege creds; require PRs and passing contract tests before merges.
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Freeze specs in living docs; add schema registry and migration checks to catch silent drift early.
Fresh architecture paradigms...
- 01.
Start agent-friendly: typed clients, explicit contracts, golden datasets, and an eval suite from day one.
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Encapsulate third-party calls, secrets, and data writes behind hand-written adapters with audit logs.
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