The Complete AI Pair-Programming Workflow: Research → Code → Review → Test

Stop Using One Model for Everything

Most people use AI for coding by pasting into one chat window and hoping. The teams getting real leverage treat it as a pipeline: research the problem, generate the code, review it adversarially, and test it — with the best-suited model handling each stage. A unified platform makes this practical because every model is one parameter away.

Below, each stage routes through Vincony so you never juggle keys or SDKs. Let the Smart Model Router pick automatically, or pin a model per stage as shown.

Stage 1 — Research

Before writing code, ground the task. Use a large-context model to read the relevant files, docs, and constraints and produce a short plan you can review.

import vincony
client = vincony.Client(api_key="YOUR_KEY")

plan = client.chat(
    model="gemini-2.5-pro",   # huge context for repo + docs
    messages=[{"role": "user", "content": "Given these files, plan a rate limiter for our API: " + context}],
).text

Stage 2 — Code

Hand the approved plan to a strong reasoning/coding model to implement it. Keep the plan in the prompt so the generation stays anchored to what you agreed on.

impl = client.chat(
    model="claude-opus-4.5",
    messages=[
        {"role": "system", "content": "Implement exactly the approved plan. No new dependencies."},
        {"role": "user", "content": plan},
    ],
).text

Stage 3 — Review

Don't let the model that wrote the code grade its own homework. Run an adversarial, multi-model review so different blind spots cancel out — the same approach covered in our multi-model code review guide.

review = client.code.review(
    code=impl,
    models=["codestral", "qwen3-coder", "gpt-5"],
    consensus=True,
)
for issue in review.issues:
    print(f"[{issue.severity}] line {issue.line}: {issue.message}")

Stage 4 — Test

Finally, generate tests that target the edge cases the review surfaced, then run them. Tests are the objective backstop that turns "looks right" into "is right."

tests = client.chat(
    model="codestral",
    messages=[{"role": "user", "content": "Write pytest cases for the edge cases flagged in this review: " + str(review.issues)}],
).text

Four stages, four well-suited models, one key and one bill. Wire up the whole pipeline with the developer API or start free and build your own.