Search result for:
All Topics
Category
Applied AI
Our picks
RAG: LLM performance boost with retrieval-augmented generation
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
AI alignment made simple: innovative solutions for businesses
AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.
LLM distillation demystified: a complete guide
LLM distillation isolates task-specific LLM performance and mirrors it in a smaller format—creating faster and cheaper performance.
Recomended for you
Why GenAI evaluation requires SME-in-the-loop for validation and trust
It’s critical enterprises can trust and rely on GenAI evaluation results, and for that, SME-in-the-loop workflows are needed. In my first blog post on enterprise GenAI evaluation, I discussed the importance of specialized evaluators as a scalable proxy for SMEs. It simply isn’t practical to task SMEs with performing manual evaluations – it can take weeks if not longer, unnecessarily…
Research spotlight: is long chain-of-thought structure all that matters when it comes to LLM reasoning distillation?
We’re taking a look at the research paper, LLMs can easily learn to reason from demonstration (Li et al., 2025), in this week’s community research spotlight. It focuses on how the structure of reasoning traces impacts distillation from models such as DeepSeek R1. What’s the big idea regarding LLM reasoning distillation? The reasoning capabilities of powerful models such as DeepSeek…
Why enterprise GenAI evaluation requires fine-grained metrics to be insightful
GenAI needs fine-grained evaluation for AI teams to gain actionable insights.
All articles on
Applied AI
Why GenAI evaluation requires SME-in-the-loop for validation and trust
It’s critical enterprises can trust and rely on GenAI evaluation results, and for that, SME-in-the-loop workflows are needed. In my first blog post on enterprise GenAI evaluation, I discussed the importance of specialized evaluators as a scalable proxy for SMEs. It simply isn’t practical to task SMEs with performing manual evaluations – it can take weeks if not longer, unnecessarily…
Research spotlight: is long chain-of-thought structure all that matters when it comes to LLM reasoning distillation?
We’re taking a look at the research paper, LLMs can easily learn to reason from demonstration (Li et al., 2025), in this week’s community research spotlight. It focuses on how the structure of reasoning traces impacts distillation from models such as DeepSeek R1. What’s the big idea regarding LLM reasoning distillation? The reasoning capabilities of powerful models such as DeepSeek…
Why enterprise GenAI evaluation requires fine-grained metrics to be insightful
GenAI needs fine-grained evaluation for AI teams to gain actionable insights.
What is specialized GenAI evaluation, and why is it so critical to enterprise AI?
Specialized GenAI evaluation ensures AI assistants meet business requirements, SME expertise, and industry regulations—critical for production-ready AI.
LLM alignment techniques: 4 post-training approaches
Ensure your LLMs align with your values and goals using LLM alignment techniques. Learn how to mitigate risks and optimize performance.
Retrieval-augmented generation (RAG) failure modes and how to fix them
Discover common RAG failure modes and how to fix them. Learn how to optimize retrieval-augmented generation systems for max business value.
What is large language model (LLM) alignment?
Learn about large language model (LLM) alignment and how it maximizes the effectiveness of AI outputs for organizations.
RAG: LLM performance boost with retrieval-augmented generation
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
Snorkel AI signs strategic collaboration agreement with AWS to help enterprises cross the demo-to-production chasm
To tackle generative AI use cases, Snorkel AI + AWS launched an accelerator program to address the biggest blocker: unstructured data.
AI alignment made simple: innovative solutions for businesses
AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.
Walking safely before building flying saucer seatbelts: introducing Enterprise Alignment
Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment
Crossing the demo-to-production chasm with Snorkel Custom
We’re excited to announce Snorkel Custom to help enterprises cross the chasm from flashy chatbot demos to real production AI value.
Five sessions not to miss at Google Cloud Next 24
Snorkel AI will be at Google Cloud Next. The event will feature more than 700 sessions, so we picked five that we think you shouldn’t miss.
Content filtering breakthrough: Snorkel client reaches 96% recall in 3 days
Snorkel AI helped a client solve the challenge of social media content filtering quickly and sustainably. Here’s how.
Fine-tuned representation models boost LLM systems. Here’s how
Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.
Ready to accelerate AI development?
Deploy production AI and ML applications 10-100x faster with Snorkel Flow,
the AI data development platform.
Request a demo