You guys — GitHub just dropped an honest post about Copilot code review getting worse because tools got better, and their explanation is wild. They tracked real data from over 10k repos across hundreds of teams for six months, tracking how many AI-generated suggestions actually get accepted versus rejected in PR reviews. At one point acceptance was hitting upwards of 90% in some places — which sounds great but was actually a red flag. Why? Because a huge chunk of that came from a culture where people were clicking accept on Copilot edits without really reading them first, essentially trusting the AI blindly and compounding every small mistake it made. They also found significant hallucination rates going straight into production because no one forced verification before merging — the classic "I'll fix it later" trap amplified by an omnipresent tool.
Their fix was smart because it didn't just blame the model; they built guardrails on both sides of the equation. On the product side, Copilot now runs a compile-and-lint check against each suggestion before presenting it as reviewable code — that alone nuked dozens of low-hanging hallucination errors at the source. They also made confidence flags explicit so you're not treated to confident hallucinations and added better feedback loops for teams to flag bad edits directly back to their model fine-tuning. On the team side, they launched a best practices program teaching developers that AI output is untrusted by default — reviewers are now explicitly expected to verify Copilot code against real tests before hitting accept. The numbers after these changes tell the story: acceptance rates in high-performing teams stabilized at a more honest level (well below 90% because bad suggestions are flagged), and the rate of bug fixes caused by accepted AI edits dropped significantly across all tracked repositories over several months — which is what we actually want to see.
They also released an open model card for their review fine-tuned version so anyone building similar tools can start from a sane baseline instead of reinventing flawed workflows. The whole thing is worth reading because it's one of the rare honest postmortems from a big tech company about where generative AI tooling actually fails in production and what real guardrails look like when you build them properly. If you use Copilot for code review, this should change how you think about every acceptance click — treat its output as an unverified suggestion that needs your eyes before it touches the main branch.
Source: https://github.blog/ai-and-ml/github-copilot/better-tools-made-copilot-code-review-worse-heres-how-we-actually-improved-it/
Their fix was smart because it didn't just blame the model; they built guardrails on both sides of the equation. On the product side, Copilot now runs a compile-and-lint check against each suggestion before presenting it as reviewable code — that alone nuked dozens of low-hanging hallucination errors at the source. They also made confidence flags explicit so you're not treated to confident hallucinations and added better feedback loops for teams to flag bad edits directly back to their model fine-tuning. On the team side, they launched a best practices program teaching developers that AI output is untrusted by default — reviewers are now explicitly expected to verify Copilot code against real tests before hitting accept. The numbers after these changes tell the story: acceptance rates in high-performing teams stabilized at a more honest level (well below 90% because bad suggestions are flagged), and the rate of bug fixes caused by accepted AI edits dropped significantly across all tracked repositories over several months — which is what we actually want to see.
They also released an open model card for their review fine-tuned version so anyone building similar tools can start from a sane baseline instead of reinventing flawed workflows. The whole thing is worth reading because it's one of the rare honest postmortems from a big tech company about where generative AI tooling actually fails in production and what real guardrails look like when you build them properly. If you use Copilot for code review, this should change how you think about every acceptance click — treat its output as an unverified suggestion that needs your eyes before it touches the main branch.
Source: https://github.blog/ai-and-ml/github-copilot/better-tools-made-copilot-code-review-worse-heres-how-we-actually-improved-it/