Comment by raw_anon_1111
2 months ago
The difference for me recently
Write a lambda that takes an S3 PUT event and inserts the rows of a comma separated file into a Postgres database.
Naive implementation: download the file from s3 and do a bulk insert - it would have taken 20 minutes and what Claude did at first.
I had to tell it to use the AWS sql extension to Postgres that will load a file directly from S3 into a table. It took 20 seconds.
I treat coding agents like junior developers.
Unlike junior developers, llms can take detailed instructions and produce outstanding results at first shot a good number of times.
While I’m pro LLMs over junior developers. The other issue with LLMs is even the most junior developer will learn your business context over time.
In my case, in consulting (cloud + app dev), I just start the AGENTS.md file with a summary of the contract (the SOW), my architectural diagram and the transcript of my design review with the customer.
Did you ask it to research best practices for this method, have an adversarial performance based agent review their approach or search for performant examples of the task first? Relying on training data only will always get your subpar results. Using “What is the most performant way to load a CSV from S3 into PostgreSQL on RDS? Compare all viable and research approaches before recommending one.” gave me the extension as the top option.
I knew the best way. I was just surprised that Claude got it wrong. As soon as I told it to use the s3 extension, it knew to add the appropriate permissions, to update my sql unit script to enable the extension and how to write the code
Yeah, give them a research project first they do pretty well. Off the cuff usually trash. I think thats the biggest disconnect between people who think AI good from bad - relying on training data memory will usually lead to subpar results.
Same pattern in data engineering generally. LLMs default to the obvious row-by-row or download-then-insert approach and you have to steer them toward the efficient path (COPY, bulk loaders, server-side imports). Once you name the right primitive, they execute it correctly, permissions and all, as you found.
The deeper issue is that "efficient ingest" depends heavily on context that's implicit in your setup: file sizes, partitioning, schema evolution expectations, downstream consumers. A Lambda doing direct S3-to-Postgres import is fine for small/occasional files, but if you're dealing with high-volume event-driven ingestion you'll hit connection pool pressure fast on RDS. At that point the conversation shifts to something like a queue buffer or moving toward a proper staging layer (S3 → Redshift/Snowflake/Databricks with native COPY or autoloader). The LLM won't surface that tradeoff unless you explicitly bring it up. It optimizes for the stated task, not for the unstated architectural constraints.
Also with Redshift - split the file up before ingestion to equal the number of nodes or combine a lot of small files into larger files before putting them into S3 and/or use an Athena CTAS command to combine a lot of small files into one big file.
So in my other case, the whole thing was
Web crawler (internal customer website) using Playwrite -> S3 -> SNS -> SQS -> Lambda (embed with Bedrock) -> S3 Vector Store.
Similar to what you said, I ran into Bedrock embedding service limits. Then once I told it that, it knew how to adjust the lambda concurrency limits. Of course I had to tell it to also adjust the sqs poller so messages wouldn’t be backed up in flight, then go to the DLQ without ever being processed.
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