How does Cosine minimise hallucinations and ensure code quality?
Cosine minimises hallucinations through rigorous evaluation, testing, and guardrails — verifying every output against the existing codebase before creating a pull request.
Cosine minimises hallucinations through rigorous evaluation, testing, and guardrails built into its agent loop. Each output is verified against the existing codebase and validated through automated tests before it ever reaches a pull request.
Multi-layered quality process
1. Context-grounded reasoning
Cosine retrieves and reads relevant files before writing code, ensuring every action is based on real context — not guesses. This grounding process dramatically reduces hallucinations that often appear in chat-based coding tools.
2. Step-by-step planning
Before writing, Cosine creates an internal plan that outlines the changes required. The plan is continuously checked against repository context, so the model stays aligned with project structure and dependencies.
3. Iterative validation
Each change is tested and refined through Cosine’s test–validate–iterate loop:
Run or generate relevant unit/integration tests.
Compare results against expected outputs.
Retry or adjust if validation fails.
4. Human-in-the-loop review
All output is submitted as a PR for review. Developers can comment, request changes, or merge, giving teams complete control over quality and production standards.
Reinforcement training for reliability
Cosine’s proprietary model, Genie, is post-trained with reinforcement learning specifically on software engineering tasks. The model learns from graded task outcomes — success, failure, efficiency — rather than from static text. This process teaches it to write, refactor, and test code like an experienced engineer.
Over thousands of iterations, Genie has been trained to:
Navigate unfamiliar codebases efficiently.
Avoid over-editing or destructive changes.
Write syntactically correct and logically consistent code.
Continuous evaluation framework
Cosine continuously measures model performance against internal benchmarks and customer tasks:
Regression testing – Automated checks across real-world engineering scenarios.
Static analysis & linting – Automated code health scans.
CI/CD validation – Integration with Jenkins, CircleCI, or GitHub Actions ensures changes pass pipeline checks.
Performance metrics feed back into model improvements, ensuring accuracy increases over time.
Why this matters
Traditional copilots generate code inline without testing or validation, often producing hallucinated outputs. Cosine operates as a closed-loop system — planning, verifying, and testing autonomously before surfacing code for human review.
The result: fewer regressions, higher test coverage, and PRs you can trust.
Related pages
→ Next: How Cosine’s model was trained
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