GenAI

Distinguishing problems with external vs. internal LLMs, and data vs non-data patterns

Structured input/output, prefilling, n-shots prompting, chain-of-thought, reducing hallucinations, etc.

Learn how to combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.

What’s the real reason AI hasn’t yet delivered on its hype?

Build language agents as graphs

Build resilient language agents as graphs.

Build resilient language agents as graphs. Contribute to langchain-ai/langgraph development by creating an account on GitHub.

Desktop app for prototyping and debugging LangGraph applications locally. - langchain-ai/langgraph-studio

New LLM Pre-training and Post-training Paradigms: A Look at How Moderns LLMs Are Trained

Use cases, techniques, alignment, finetuning, and critiques against LLM-evaluators.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Learn how to do batch inference using a provisioned throughput endpoint.

Learn the overview and basic steps for performing model serving on Databricks.

dbdemos - Databricks Lakehouse demos : Fine Tune your LLMs with Mosaic AI Model Training

dbdemos - Databricks Lakehouse demos : LLM Chatbot With Retrieval Augmented Generation (RAG) and DBRX

Discover the power of Lakehouse. Install demos in your workspace to quickly access best practices for data ingestion, governance, security, data science and data warehousing.

Web site created using create-react-app

The easiest & fastest way to run customized and fine-tuned LLMs locally or on the edge

My experiences from preparing for and successfully passing the (beta) exam

Discover why GraphRAG will subsume vector-only RAG and emerge as the default RAG architecture for most use cases.

Educational resources on LLMs

Leverage Azure Cosmos DB for generative AI workloads for automatic scalability, low latency, and global distribution to handle massive data volumes and real-...

GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more:

I summarise the kinds of evaluations that are needed for a structured data generation task.

Deep dive into self-improving evaluators in LangSmith, motivated by the rise of LLM-as-a-Judge evaluators plus research on few-shot learning and aligning human preferences.

Read our roadmap on the next wave of enterprise data software in the age of AI.