How to Perform Automated Code Review with LLMs: Tools and Prompts

Introduction: Why Automated Code Review with LLMs Matters Today

Modern development teams need to deliver code faster than ever while maintaining high standards of quality and security. Manual code reviews, though valuable, can’t keep up with the volume of changes generated by today’s CI/CD processes. Large Language Models (LLMs) now offer real-time code analysis, suggestion generation, and bug fixing, paving the way for automated code review that complements human judgment.

This article explores the latest LLM models available in 2026, teaches you how to craft effective prompts for code review, and provides practical examples you can immediately copy and adapt in your workflows.

The Best LLMs for 2026: Muse Glimmer vs. SeedRealtime

Meta AI Muse Glimmer (30B, Apache 2.0)

ByteDance SeedRealtime: Full-Duplex Audio-Visual LLM

SeedRealtime combines textual, audio, and visual understanding into a single model, enabling multimodal code review. Unlike inspect, which relies solely on text, SeedRealtime can analyze screenshots of user interfaces, audio recordings of pair-programming sessions, and even video streams of demos, generating contextual annotations directly in the source code.

How to Structure a Prompt for Code Review

A good prompt is specific, structured, and context-aware. The following structure works with both Muse Glimmer and SeedRealtime and can be adapted to any LLM that supports text-in-text-out.

## Role
You are a senior software engineer specializing in automated code review. You are an expert in coding best practices, security, and performance.

## Input
- Language: {language}
- File: {file_path}
- Code:
```
{code_snippet}
```
- Context: {branch_name} / {commit_hash}
- Existing errors: {eventuali_errori_ci}

## Output
Provide:
1. Overall rating (0-5)
2. Bulleted list of specific issues with severity and suggested location.
3. Example fix in diff-style format.
4. Notes on performance impact and security.

## Style
Use a professional tone, keep comments concise, and follow repository conventions.

When preparing the prompt, always include:

Here’s an example of integrating this into a CI workflow:

#!/usr/bin/env bash
# run_llm_review.sh
MODEL=muse-glimmer
PROMPT="$(cat

End-to-End Workflow: From Commit to Commented Review

1. Trigger
2. Extraction
3. Prompt preparation
4. Inference
5. Post-processing
6. Comment
7. Monitoring

Using an observability platform likeLangSmithorLangfusehelps you monitor review quality and optimize prompts over time.

Try It Out Now: A Mini Project

1. Clone an open-source repository that uses modern linters (e.g., thefastapirepository on GitHub).

2. Create a script that extracts the latest commit, prepares the prompt, and sends the request to Muse Glimmer.

3. Display the resulting review comment in a Markdown file and compare the results with Pylint or Black.

4. Record latencies and false positive rates in Langfuse.

This demo project is perfect for a hackathon or an internal exploration session.

Key Takeaways

• Choose the right model
• Structure the prompt
• Integrate into CI
• Monitor performance
• Experiment and iterate

Conclusion

Modern LLMs, such as Meta’s Muse Glimmer and ByteDance’s SeedRealtime, have made automated code review a daily reality in 2026. With the right prompts, a well-designed CI workflow, and a human eye to catch edge cases, teams can significantly improve code quality, reduce technical debt, and accelerate time-to-market. Start with an existing repository, experiment with prompt structure, and watch how LLMs transform code review from a manual process into an intelligent, always-available assistant.