is a relatively new sub-field in AI, focused on understanding how neural networks function by reverse-engineering their internal mechanisms and representations, aiming to translate them into human-understandable algorithms and concepts. This is in contrast…

Conceptual overview and practical considerations

The exciting new world of designing conversation driven APIs for LLMs.

Are LLMs good or bad for our mental health? It’s more complicated than that. The post LLMs and Mental Health appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

Exaone 4.0 is redefining AI for business users with unmatched performance in science, math, and coding. How does it stack up against the competition?

What happens when you stop concatenating and start decomposing: a new way to think about attention. The post Mechanistic View of Transformers: Patterns, Messages, Residual Stream… and LSTMs appeared first on Towards Data Science. Generated by RSStT. The copyright…

Metrics to track for RAG and agents, plus the frameworks that help The post Agentic AI: On Evaluations appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

An overview of popular techniques to confine LLMs' output to a predefined schema The post Generating Structured Outputs from LLMs appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

Do RAG without doing RAG with this powerful new NLP and data extraction library The post Introducing Google’s LangExtract tool appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

Improve your LLM by optimizing its context The post How to Create Powerful LLM Applications with Context Engineering appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source