FAQ¶

Q: Can Qodo Merge serve as a substitute for a human reviewer?

Answer:1¶

Qodo Merge is designed to assist, not replace, human reviewers.

Reviewing PRs is a tedious and time-consuming task often seen as a "chore". In addition, the longer the PR – the shorter the relative feedback, since long PRs can overwhelm reviewers, both in terms of technical difficulty, and the actual review time. Qodo Merge aims to address these pain points, and to assist and empower both the PR author and reviewer.

However, Qodo Merge has built-in safeguards to ensure the developer remains in the driver's seat. For example:

Preserves user's original PR header
Places user's description above the AI-generated PR description
Won't approve PRs; approval remains reviewer's responsibility
The code suggestions are optional, and aim to:
- Encourage self-review and self-reflection
- Highlight potential bugs or oversights
- Enhance code quality and promote best practices

Answer:2¶

Modern AI models, like Claude Sonnet and GPT-4, are improving rapidly but remain imperfect. Users should critically evaluate all suggestions rather than accepting them automatically.
AI errors are rare, but possible. A main value from reviewing the code suggestions lies in their high probability of catching mistakes or bugs made by the PR author. We believe it's worth spending 30-60 seconds reviewing suggestions, even if some aren't relevant, as this practice can enhance code quality and prevent bugs in production.
The hierarchical structure of the suggestions is designed to help the user quickly understand them, and to decide which ones are relevant and which are not:
- Only if the Category header is relevant, the user should move to the summarized suggestion description.
- Only if the summarized suggestion description is relevant, the user should click on the collapsible, to read the full suggestion description with a code preview example.
In addition, we recommend to use the extra_instructions field to guide the model to suggestions that are more relevant to the specific needs of the project.
The interactive PR chat also provides an easy way to get more tailored suggestions and feedback from the AI model.

Q: How can I get more tailored suggestions?

Answer:3¶

See here for more information on how to use the extra_instructions and best_practices configuration options, to guide the model to more tailored suggestions.

Q: Will you store my code? Are you using my code to train models?

Answer:4¶

No. Qodo Merge strict privacy policy ensures that your code is not stored or used for training purposes.

For a detailed overview of our data privacy policy, please refer to this link

Q: Can I use my own LLM keys with Qodo Merge?

Answer:5¶

When you self-host the open-source version, you use your own keys.

Qodo Merge with SaaS deployment is a hosted version of Qodo Merge, where Qodo manages the infrastructure and the keys. For enterprise customers, on-prem deployment is also available. Contact us for more information.

Q: Can Qodo Merge review draft/offline PRs?

Answer:6¶

Yes. While Qodo Merge won't automatically review draft PRs, you can still get feedback by manually requesting it through online commenting.

For active PRs, you can customize the automatic feedback settings here to match your team's workflow.

Q: Can the 'Review effort' feedback be calibrated or customized?

Answer:7¶

Yes, you can customize review effort estimates using the extra_instructions configuration option (see documentation).

Example mapping:

Effort 1: < 30 minutes review time
Effort 2: 30-60 minutes review time
Effort 3: 60-90 minutes review time
...

Note: The effort levels (1-5) are primarily meant for comparative purposes, helping teams prioritize reviewing smaller PRs first. The actual review duration may vary, as the focus is on providing consistent relative effort estimates.

Q: How to reduce the noise generated by Qodo Merge?

Answer:3¶

The default configuration of Qodo Merge is designed to balance helpful feedback with noise reduction. It reduces noise through several approaches:

Auto-feedback uses three highly structured tools (/describe, /review, and /improve), designed to be accessible at a glance without creating large visual overload
Suggestions are presented in a table format rather than as committable comments, which are far noisier
The 'File Walkthrough' section is folded by default, as it tends to be verbose
Intermediate comments are avoided when creating new PRs (like "Qodo Merge is now reviewing your PR..."), which would generate email noise

From our experience, especially in large teams or organizations, complaints about "noise" sometimes stem from the following issues:

Feedback from multiple bots: When multiple bots provide feedback on the same PR, it creates confusion and noise. We recommend using Qodo Merge as the primary feedback tool to streamline the process and reduce redundancy.
Getting familiar with the tool: Unlike many tools that provide feedback only on demand, Qodo Merge automatically analyzes and suggests improvements for every code change. While this proactive approach can feel intimidating at first, it's designed to continuously enhance code quality and catch bugs and problems when they occur. We recommend reviewing this guide to help align expectations and maximize the value of Qodo Merge's auto-feedback.

Therefore, at a global configuration level, we recommend using the default configuration, which is designed to reduce noise while providing valuable feedback.

However, if you still find the feedback too noisy, you can adjust the configuration. Since each user and team has different needs, it's definitely possible - and even recommended - to adjust configurations for specific repos as needed. Ways to adjust the configuration for noise reduction include for example:

Note that some users may prefer the opposite - more thorough and detailed feedback. Qodo Merge is designed to be flexible and customizable, allowing you to tailor the feedback to your team's specific needs and preferences. Examples of ways to increase feedback include: