Elicit: AI for scientific research

Engineering

Mar 30, 2026

Engineering interviews in the era of agents
Two years ago we published our stance on coding assistants in interviews. A lot has changed…

Jan 30, 2025

Against RL: The Case for System 2 Learning
Reinforcement learning may boost LLMs today, but it cannot deliver safe, long-term intelligence. We argue for System 2 learning instead.

Jul 23, 2024

Trust at scale: Auto-evaluation for high-stakes LLM accuracy
Elicit develops LLM-based auto-evals to balance scale, trust, and flexibility, ensuring reliable scientific reasoning at superhuman speed.

Jun 22, 2024

How to Hire AI engineers
Not every AI role needs a PhD in ML. A good AI engineer can bridge full-stack skills with LLM expertise, making AI practical in real products.

Jun 13, 2024

From grep to SPLADE: A Journey Through Semantic Search
Elicit implements SPLADE to leverage semantic search while retaining consistent rigor and accuracy.

Jan 11, 2024

Coding Assistants in Technical Interviews: Our Stance
Elicit's nuanced stance on coding assistants in interviews: welcome as tools for testing insight, but not as replacements for genuine reasoning.

Dec 20, 2023

Build a Search Engine, Not a Vector DB
Don’t confuse embeddings with memory. To build effective RAG, start with a search engine, not just a vector database.