Yan Cui
How we built an AI-powered code reviewer in 80 hours
#1about 2 minutes
An overview of an AI-powered code reviewer
The tool reviews pull requests, leaves inline comments, and provides a dashboard, with the talk focusing on lessons learned from building it.
#2about 3 minutes
The high-level serverless architecture for the application
The system uses GitHub webhooks, EventBridge, Lambda, Amazon Bedrock, and DynamoDB, with Clerk for auth and Stripe for payments.
#3about 4 minutes
Choosing Amazon Bedrock for security and privacy
Amazon Bedrock was selected for its strong security guarantees, data privacy policies, and serverless, token-based pricing model suitable for sensitive customer code.
#4about 5 minutes
The truth about LLM context window size and reasoning
Large context window sizes are misleading because a model's ability to reason over content collapses long before the advertised limit, forcing a one-prompt-per-file strategy.
#5about 3 minutes
Managing API rate limits and model availability
To overcome low default API rate limits, strategies include requesting limit increases, using cross-region inference, and implementing fallbacks to other models for reliability.
#6about 3 minutes
Strategies for controlling high LLM costs
The most effective cost control measure is to analyze only the changed lines in a pull request rather than the entire file, which also improves user experience.
#7about 4 minutes
Handling timeouts with durable execution in Lambda
A lightweight durable execution mechanism using checkpoints in DynamoDB prevents reprocessing files on Lambda retries, which are common due to slow LLM response times.
#8about 4 minutes
Dealing with different types of LLM hallucinations
Hallucinations range from simple invalid JSON to complex errors like suggesting fixes for outdated libraries, which can be mitigated with RAG but at a significant cost.
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Incentivizing automation with a 'keep what you kill' policy
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Building and iterating on an LLM-powered product
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