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Large Language Model (LLM) Coding Assistance

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Note: It has been about three months since this was originally written, so there is a certain amount of information that is out of date. See the addendum for updated information. With all the hype surrounding Generative AI/LLM, and all the hallucinations mentioned in the news, what are these actually good for? As it turns out LLMs trained for code generation are helpful. But what if you don’t want your code going to some cloud provider? The following is a…

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Transforming Data with Amazon Athena

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My prior posts used Lambda to do data transformation. But what if we could use a non-programmatic tool, in keeping with the Extract-Load-Transform mindset of the modern data pipeline. As it turns, we can: Amazon Athena can write data as well as query it. There are, of course, a few stumbles along the way. In this blog post I walk through the process of aggregating CloudTrail data using SQL.

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From RAGs to Riches – Adding Context to Your LLM

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In my previous post, Experiences in Fine-Tuning LLMs: Time + Power = Potato?, I covered my experiences around trying to fine-tune an LLM (large language model) with a dataset, which gave me less than stellar results. Ultimately, fine-tuning is best for a use-case where additional reasoning & logic needs to be added to an LLM, but it’s subpar for adding information. However, if you’re trying to get an LLM to answer questions using your data, then retrieval augmented generation (RAG)…

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Experiences in Fine-Tuning LLMs: Time + Power = Potato?

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Embarking on the journey to fine-tune large language models (LLMs) can often feel like setting sail into uncharted waters, armed with hope and a map of best practices. Yet, despite meticulous planning and execution, the quest for improved performance doesn’t always lead to the treasure trove of success one might anticipate. And I know you may be wondering how potatoes come into play here, but I promise that we’ll get to it. From the challenges of data scarcity to resource…

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PostgreSQL Text Search

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Introduction A common problem in software development is searching through text documents. For example, if you have a database of recipes, you might want to search by one or more ingredients, or if you have a collection of server log files, you might want to search for all errors that did not come from the database. This type of functionality is called “text search”. There are a lot of text search libraries like Lucene, or applications like ElasticSearch (which is…

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Aggregating Files in your Data Lake – Part 1

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As I’ve written in the past, large numbers of small files make for an inefficient data lake. But sometimes, you can’t avoid small files. Our CloudTrail repository, for example, has 4,601,675 files as-of this morning, 44% of which are under 1,000 bytes long. In this post, I develop a Lambda-based data pipeline to aggregate these files, storing them in a new S3 location partitioned by date. Along the way I call out some of the challenges that face such a pipeline.

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