AI Video Coding Agents with Taste: The Next Frontier in AI Development
What if your coding agent didn't just spit out working code, but wrote it like you do? Same folder structure, same naming habits, same tiny quirks you've picked up over years of shipping. That's the pitch behind "taste" in AI coding agents-teaching an agent to internalize your style, not just your specs.
This idea comes from years of building AI agents and running into the same wall: LLMs can generate code, but the output often feels generic. It runs, but it doesn't feel like your work. You spend cycles rewriting, refactoring, and reminding the model of your unwritten rules.
Why most AI code still wastes your time
LLMs default to the shortest path to "works." That means missing your early-return habit, skipping that small utility you always extract, or flattening a folder structure you'd never ship. You end up prompting harder or fixing it by hand. Either way, your flow gets taxed.
The real cost isn't correctness. It's taste-your naming conventions, file boundaries, dependency choices, and the invisible logic behind them.
What "taste" looks like in practice
A head-to-head demo says it all. The task: build a simple CLI that prints the date in ISO format. A generic LLM (Claude Code) returns a bare JavaScript script with a console log. It works. It's also forgettable.
A taste-driven agent (CommandCode) takes a different route. It reaches for TypeScript, bundles with tsup, wires up commander.js, adds an ASCII banner, sets version to 0.0.1, and splits commands into their own directory. It mirrors the developer's patterns without being told, because it has learned them.
How a taste model learns
This isn't a static rules engine. It combines neural intuition with symbolic constraints, reinforced by ongoing feedback as you accept, edit, or reject changes. Think continuous reinforcement learning backed by a neuro-symbolic layer that captures your "invisible architecture of choices."
The loop is simple: generate, observe your edits, reflect, and adjust context. Over time, the agent builds a sense of what "looks right" before you say a word. For background, see reinforcement learning and neuro-symbolic AI.
Rules won't save you. "Vibe prompting" won't either.
Hard rules are brittle and incomplete. You'll always miss edge cases, and maintenance turns into overhead. On the other side, stuffing prompts with preferences helps a little, then collapses under real work.
Taste sits in the middle-adaptive, living, and shaped by your actual edits. It scales with you and your team instead of asking you to babysit it.
Taste Repos: sharing style like you share code
The long view is an ecosystem of "Taste Repos." You could adopt a "React taste" from a trusted expert or align an entire org on a "Design Engineer taste" that influences every generated line. Not a static .md, but a model that updates as practices shift.
This opens up a new kind of collaboration: shareable, testable preferences that travel across projects and teams without nagging style debates in every PR.
What this means for engineering leaders
- Codify the obvious: expose naming, folder conventions, and dependency picks through starter repos and templates the agent can study.
- Instrument feedback: add clear signals in PRs (labels, comments, quick checkboxes) so the agent can learn from approvals and edits.
- Guardrails first: define non-negotiables (security policies, lint rules, license constraints) so "taste" never conflicts with compliance.
- Measure fit, not just pass/fail: track edit distance from agent output to merged code, review time, and rework rate per module.
- Iterate in public: keep a changelog of preference shifts so your taste model evolves with the codebase, not against it.
Why this matters now
As AI takes on more of the typing, the leverage moves to intent and style. The closer an agent matches your taste, the fewer round trips you need. That means shorter reviews, cleaner diffs, and code that lands closer to done on the first pass.
LLMs captured text. Taste models capture intent. For developers, that's where the real efficiency hides.
Next steps
- Seed a "taste corpus": your best repos, refactor PRs, and internal templates. Let the agent learn from your strongest work, not random history.
- Start narrow: pick one stack (e.g., TypeScript CLIs) and one repo. Ship wins there before you expand.
- Automate reflection: after each merge, feed the agent the diff, review notes, and final structure so it adapts quickly.
If you're upskilling teams on AI-assisted coding and review workflows, explore practical training and certifications for devs here: AI Certification for Coding and curated tool lists for builders here: AI tools for generative code.
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