RAG pipeline tutorial: Common pitfalls (and how to fix them)
RAG tutorial: build a LangChain Q&A pipeline over your docs—chunking, embeddings, retrieval, citations, and Weave evaluation, pitfalls fixed.
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Se connecter pour noterRAG tutorial: build a LangChain Q&A pipeline over your docs—chunking, embeddings, retrieval, citations, and Weave evaluation, pitfalls fixed.
Build a human-in-the-loop customer service workflow with LangGraph and W&B Weave: risk-scored AI replies that auto-send or pause for human review, with evaluation and dashboards.
A comprehensive guide to what AI Guardrails actually do, where they cost you, and when to skip them entirely.
Or, how I trained an agent to research AI
A case study in treating representation as a design decision, not a given — with the code and the logging to prove it.
If you want the shortest path from “curious about Weave” to “looking at a trace," well, you're in luck
Build a working RAG system from scratch with FAISS and W&B Weave: chunking, embeddings, retrieval, grounded generation with citations, and a traced evaluation.
An introduction to agentic RAG: retrieval inside an agent loop, core architecture components, worked Python examples traced with W&B Weave, and when not to use it.
How GPT-5.6, GPT-5.5, and GLM-5.2 compare on repository-level coding: hidden-test pass rates, reliability, latency, and estimated cost, measured with W&B Weave.
Machine learning for fraud detection in banking
Learn how to create synthetic instruction datasets for fine-tuning, inspired by the Self-Instruct framework, and trace every generation with W&B Weave. This is the first article in our fine-tuning series.
Analyzing World Action Models’ ability to zero-shot learn new tasks.
