The AIKEA Effect

About The AIKEA Effect

This is a 2023 essay by Artur Piszek that argues developers and companies often over-engineer projects that use large language models. It describes a set of simpler tasks, such as synthesizing documents, answering questions over a proprietary dataset, and extracting relevant passages from long documents, which the author says can provide immediate business value. The essay also argues that speed of execution, distribution, and marketing matter more than architecture or the underlying technology.

The essay is an opinion piece without implementation details, code, or data. Its claims about the IKEA effect and the performance of specific models are presented as the author's view and are not backed by detailed sources in the text. It was written in August 2023 and refers to models from that period (GPT-3.5, GPT-4, and LLAMA2).

Key features

• Argues that simple LLM tasks like document synthesis can deliver immediate business value
• Lists question answering over a proprietary dataset as a practical LLM use case
• Suggests dashboards that surface key points from large document collections
• Describes converting unstructured data into structured formats to connect separate systems
• Critiques reaching for fine-tuning or custom infrastructure when prompting would suffice
• Argues that distribution, marketing, and speed of execution can be lasting advantages

The AIKEA Effect at a glance