Context Engineering for Agents involves getting relevant data into the LLM’s prompt and builds on in-context learning capabilities of LLMs. But LLMs have finite sized context windows, so you can’t just dump unprocessed context data into your Agent’s LLM prompt. You need to select the right data, process it into the correct format, and compress or summarize the data before its use as context data.
In this talk, we will introduce techniques for selection, preprocessing, and compression of context data, taking inspiration from the tried and tested techniques used for feature engineering for ML. What goes around, comes around.
Context Engineering for Agents involves getting relevant data into the LLM’s prompt and builds on in-context learning capabilities of LLMs. But LLMs have finite sized context windows, so you can’t just dump unprocessed context data into your Agent’s LLM prompt. You need to select the right data, process it into the correct format, and compress or summarize the data before its use as context data.
In this talk, we will introduce techniques for selection, preprocessing, and compression of context data, taking inspiration from the tried and tested techniques used for feature engineering for ML. What goes around, comes around.
Jim Dowling
Jim Dowling is CEO of Hopsworks and a former Associate Professor at KTH Royal Institute of Technology. He is the organizer of the annual feature store summit and co-organizer of PyData Stockholm. He is the author of an O’Reilly book on building ML systems: batch, real-time and LLMs.
Outline
Citation
@online{bochman2025,
author = {Bochman, Oren},
title = {From {Feature} {Engineering} to {Context} {Engineering} for
{Agents}},
date = {2025-12-09},
url = {https://orenbochman.github.io/posts/2025/2025-12-09-pydata-from-feat-engineering-to-context-engineeting-for-agents/},
langid = {en}
}