Ensuring AI Code Quality with SonarQube + Gemini Code Assist

 Posted on 4 March 2025

In my previous Code Quality in the Age of AI-Assisted Development blog post, I talked about how generative AI is changing the way we code and its potential impact on code quality. I recommended using static code analysis tools to monitor AI-generated code, ensuring its security and quality. In this blog post, I will explore one such static code analysis tool, SonarQube, and see how it improves the quality of AI-generated code. Read More →
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Code Quality in the Age of AI-Assisted Development

 Posted on 28 January 2025

As developers transition from manual coding to AI-assisted coding, an increasing share of code is now being generated by AI. This shift has significantly boosted productivity and efficiency, but it raises an important question: how does AI-assisted development impact code quality? How can we ensure that AI-generated code maintains high quality, adheres to good style, and follows best practices? This question has been on my mind recently, and it is the topic of this blog post. Read More →
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Improve the RAG pipeline with RAG triad metrics

 Posted on 21 January 2025

In my previous RAG Evaluation - A Step-by-Step Guide with DeepEval post, I showed how to evaluate a RAG pipeline with the RAG triad metrics using DeepEval and Vertex AI. As a recap, these were the results: Answer relevancy and faithfulness metrics had perfect 1.0 scores whereas contextual relevancy was low at 0.29 because we retrieved a lot of irrelevant context: The score is 0.29 because while the context mentions relevant information such as "The Cymbal Starlight 2024 has a cargo capacity of 13. Read More →
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RAG Evaluation - A Step-by-Step Guide with DeepEval

 Posted on 14 January 2025

In my previous Evaluating RAG pipelines post, I introduced two approaches to evaluating RAG pipelines. In this post, I will show you how to implement these two approaches in detail. The implementation will naturally depend on the framework you use. In my case, I’ll be using DeepEval, an open-source evaluation framework. Approach 1: Evaluating Retrieval and Generator separately As a recap, in this approach, you evaluate the retriever and generator of the RAG pipeline separately with their own separate metrics. Read More →
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Evaluating RAG pipelines

 Posted on 9 January 2025

Retrieval-Augmented Generation (RAG) emerged as a dominant framework to feed LLMs the context beyond the scope of its training data and enable LLMs to respond with more grounded answers with less hallucinations based on that context. However, designing an effective RAG pipeline can be challenging. You need to answer certain questions such as: How should you parse and chunk text documents for embedding? What chunk and overlay size to use? Read More →
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Gemini on Vertex AI and Google AI now unified with the new Google Gen AI SDK

 Posted on 17 December 2024

If you’ve been working with Gemini, you’ve likely encountered the two separate client libraries for Gemini: the Gemini library for Google AI vs. Vertex AI in Google Cloud. Even though the two libraries are quite similar, there are slight differences that make the two libraries non-interchangeable. I usually start my experiments in Google AI and when it is time to switch to Vertex AI on Google Cloud, I couldn’t simply copy and paste my code. Read More →
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Control LLM output with LangChain's structured and Pydantic output parsers

 Posted on 9 December 2024

In my previous Control LLM output with response type and schema post, I talked about how you can define a JSON response schema and Vertex AI makes sure the output of the Large Language Model (LLM) conforms to that schema. In this post, I show how you can implement a similar response schema using LangChain’s structured output parser with any model. You can further get the output parsed and populated into Python classes automatically with the Pydantic output parser. Read More →
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Tracing with Langtrace and Gemini

 Posted on 27 November 2024

Large Language Models (LLMs) feel like a totally new technology with totally new problems. It’s true to some extent but at the same time, they also have the same old problems that we had to tackle in traditional technology. For example, how do you figure out which LLM calls are taking too long or have failed? At the bare minimum, you need logging but ideally, you use a full observability platform like OpenTelemetry with logging, tracing, metrics and more. Read More →
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Batch prediction in Gemini

 Posted on 18 November 2024

LLMs are great in generating content on demand but if left unchecked, you can be left with a large bill at the end of the day. In my Control LLM costs with context caching post, I talked about how to limit costs by using context caching. Batch generation is another technique you can use to save time at a discounted price. What’s batch generation? Batch generation in Gemini allows you to send multiple generative AI requests in batches rather than one by one and get responses asynchronously either in a Cloud Storage bucket or a BigQuery table. Read More →
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LLM Guard and Vertex AI

 Posted on 11 November 2024

I’ve been focusing on evaluation frameworks lately because I believe that the hardest problem while using LLMs is to make sure they behave properly. Are you getting the right outputs grounded with your data? Are outputs free of harmful or PII data? When you make a change to your RAG pipeline or to your prompts, are outputs getting better or worse? How do you know? You don’t know unless you measure. Read More →
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