90 days of the enterprise, pro and team plans free, no card needed. See the plans
Blog
Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
Get the morning and evening issues
Every new post of the day, in one email. Confirmation required.
Agents2 min read
Amazon Quick now emphasizes prompt engineering principles to improve AI response accuracy and consistency across its features.
From AWS machine learning blog
Agents2 min read
Amazon Quick offers tools for prompt engineering across research, workflows, and analytics. Proper prompt design enhances output quality and efficiency.
From AWS machine learning blog
Photo: Yan Krukau
LLMs2 min read
Research shows ordinary typos rotate hidden state vectors by 43 to 56 degrees, causing prompt injection probes to drop true positive rates by up to 12 percentage points.
From arXiv cs.CL
Agents2 min read
This post outlines a framework for customizing generative AI models on AWS, ranging from simple prompt engineering to training custom models. The spectrum guides engineers through the appropriate level of investment based on workload requirements and data needs.
From AWS machine learning blog
Research1 min read
A new test-time method enhances LLM explanation faithfulness by removing uncredited concepts from input, applicable without model modifications, and tested across datasets and models.
From arXiv cs.AI
Agents1 min read
LangSmith’s product homepage has been redesigned to better align with developer workflows focused on observability, evaluation, and prompt engineering. New resource tags provide flexible grouping and filtering capabilities within workspaces, improving resource management and accessibility.
From LangChain blog
Agents1 min read
LangSmith’s new LLM-as-a-Judge evaluators incorporate self-improvement through stored human corrections, reducing prompt engineering and enabling adaptive alignment with user preferences. This streamlines evaluation for generative LLM applications.
From LangChain blog
Agents1 min read
Moving LLMs from prototype to production requires a shift in evaluation strategy. Focus on aligning the system’s goals with user needs and operational constraints, using repeatable experiments and clear metrics to avoid misinterpreting performance improvements.
From GitHub blog: AI & ML
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
Archive (100)
