Build smarter and more reliable AI applications using RAG
Enhance your LLMs with relevant, purposefully selected knowledge
Learn how to build a RAG system
RAG (Retrieval Augmented Generation) combines information retrieval with advanced text generation to enhance AI responses. When a user asks a question, the system first searches through a curated knowledge base to find relevant information and context. The retrieved information is then seamlessly integrated with a large language model’s processing capabilities to construct responses that incorporate both stored knowledge and generated text.
Benefits of using RAG
- Minimize AI hallucinations and improve factual accuracy in application responses.
- Reduce costs compared to fine-tuning by requiring less computational resources and training time.
- Provide fresh context data ensuring that generated output includes up-to-date information.
How to create RAG applications with Weave
Iterate for continuous improvement
W&B Traces and W&B Evaluations allow you to record LLM inputs, outputs, metadata, and code facilitating comprehensive analysis, iteration, and optimization of your AI application.
Track everything
Weave Models combine data and code, providing a structured way to version your application so you can more systematically keep track of your experiments.
Support safety and compliance
W&B Guardrails act as real-time safety checks on LLM input and output protecting both users and your AI application from harm.
Trusted by the leading teams across industries—from financial institutions to eCommerce giants
Socure, a graph-defined identity verification platform, uses Weights & Biases to streamline its machine learning initiatives, keeping everyone’s wallets a little more secure.
Qualtrics, a leading experience management company, uses machine learning and Weights & Biases to improve sentiment detection models that identify gaps in their customers’ business and areas for growth.
Invitae, one of the fastest-growing genetic testing companies in the world, uses Weights & Biases for medical record comprehension leading to a better understanding of disease trajectories and predictive risk.
See Weights & Biases in action
What is Retrieval Augmented Generation (RAG)?
Model-based evaluation of RAG applications
RAG++ : From POC to production
How to optimize AI performance with W&B Weave
The Weights & Biases end-to-end AI developer platform
- Weave: Debug agents and AI applications
- Traces: Explore and debug LLMs
- Evaluations: Rigorous evaluations of agentic AI systems
- Playground: Explore prompts and models
- Agents: Observability tools for agentic systems
- Guardrails: Block prompt attacks and harmful outputs
- Monitors: Continuously improve in prod
- Models: Track and visualize your ML experiments
- Sweeps: Optimize your hyperparameters
- Tables: Visualize and explore your ML data
- Core: Explore hosted, open-source LLMs
- Registry: Publish and share your AI models and datasets
- Artifacts: Version and manage your AI pipelines
- Reports: Document and share your AI insights
- SDK: Log AI experiments and artifacts at scale
- Automations: Trigger workflows automatically
The Weights & Biases platform helps you streamline your workflow from end to end
Models
Experiments
Track and visualize your ML experiments
Sweeps
Optimize your hyperparameters
Registry
Publish and share your ML models and datasets
Automations
Trigger workflows automatically
Weave
Traces
Explore and debug LLMs
Evaluations
Rigorous evaluations of GenAI applications
Core
Artifacts
Version and manage your ML pipelines
Tables
Visualize and explore your ML data
Reports
Document and share your ML insights
SDK
Log ML experiments and artifacts at scale