- Entry date
- 15 December 2022
- Category
- Method
- Access
- 🔓 Public
We built a tool to draft blog posts. It quietly proved something bigger: if an LLM can draft a paragraph, it can draft a function.
- Dec 2022
- first hands-on LLM API contact
- 3
- content formats attempted: posts, SEO plans, creative writing
- 1
- side discovery that became the whole arc: code, not content
Honest evaluation
The content-generation tool worked as built; the bigger code-generation insight it's remembered for was a conceptual hunch here, tested only later.
What would prove or disprove it further
What would prove or disprove it further: the code-generation half of the claim is arguably already settled by history — the entire Codex/Copilot-era industry effectively reran this test at scale and confirmed it. Within this portfolio specifically, the falsifiable next step was to point the same GPT-3 API at a small, defined coding task and check whether the output ran — a test this project never ran itself, but one the field answered a few months later anyway.
The evidence — full reasoning behind the verdict
Verdict: partly-proven. The narrow claim — a GPT-3-backed tool can draft readable, on-topic blog posts and SEO plans from a plain-English brief — worked and is proven. The bigger claim this write-up is actually remembered for — that an LLM API is a general instruction-follower and the same mechanism should work for code, not just prose — was never tested inside this project; it stayed a hunch.
The mechanism behind the proven half is straightforward: watching an API turn a natural-language instruction into structured, usable output repeatably, for content, is what the working pipeline demonstrated. The mechanism behind the unproven half is exactly its absence — no code was generated or evaluated here, so "would this work for code?" is recorded honestly as a side observation, not a result.
Every long arc has a first small step. This is ours: a Python script that used the newly available GPT-3 API to write blog posts — and, almost by accident, showed what an LLM API was actually good for.
What it was
A short proof of concept, built across December 2022 and January 2023: a Python tool wired to the GPT-3 API and python-docx, aimed squarely at content generation — blog posts, SEO plans, a bit of creative writing. Nothing more ambitious than that was intended at the time.
What we built
ResearchA small pipeline: prompt GPT-3 for a piece of content, format it with python-docx, hand back a document. It worked, in the narrow sense a content tool needs to work — readable, on-topic output from a plain-English brief.
What mattered wasn't the blog posts. It was the mechanics underneath them: watching an API turn a natural-language instruction into structured, usable output, repeatably. That's a generic capability, not a content-specific one — and once you've seen it work for prose, the question "would this work for code?" isn't far behind.
What we learned — including the honest negative
This was a content project, and the honest tally is short: it drafted blog posts and SEO plans reasonably well, and nothing here was engineered to write code. The code-generation insight was a side discovery, not the original goal — read that as a caveat as much as a credit. No production system, no shipped tool, came directly out of this experiment.
What it did produce was a reframing: an LLM API is a general instruction-follower, not a content-specific tool. Once that reframing landed, the natural next question was task decomposition and autonomous execution — attempted for the first time seven months later.
Where it went / status
Archived as a completed proof of concept — the tool itself was never developed further. Its importance is entirely upstream: this is the first hands-on LLM API experience in the record, and the pivot point the rest of the arc traces back to.
What is still open — kept visible
The honest edges, next to the wins. This is what turns 🔬 into 🟢 — honestly.
- It started as a content-generation tool, not a code tool — the code-generation insight was a side discovery, not the original goal.
- No code was written by the tool itself; the insight here was conceptual, proven out only in later projects.
- Nothing from this experiment shipped — it is kept in the record as the origin point, not as a result on its own merits.
Where this connects
Sources
- vocabotics project audit — OpenAI GPT-3 Experiments (first hands-on LLM API contact), Dec 2022vocabotics internal project history · as of December 2022