Guides
The guides section is work-in-progress. We want to collect all end-to-end examples of how to use Neural Inverse here including cookbooks, videos and other external resources.
Introduction to Neural Inverse
If you are new to Neural Inverse, watch these walkthroughs to get a thorough introduction to the Neural Inverse platform.
Evaluation Tutorials
Start with the fundamentals and progress through advanced techniques as your application complexity grows. Read our blog post for an overview.
Error Analysis
Review traces to classify issues like hallucinations, irrelevance, and formatting errors. Turn raw logs into actionable insights.
Automated Evaluations
Automated evaluators give you a scalable way to measure and monitor your application’s failure modes.
Testing LLM Applications
Build a testing foundation with deterministic checks and probabilistic ones using LLM judges to prevent regressions.
CI/CD for Experiments
Run Neural Inverse experiments in CI/CD pipelines to catch quality regressions before they reach production.
Synthetic Dataset Generation
Scale your test coverage with LLM-generated synthetic data. Create diverse inputs and adversarial cases without waiting for users.
Experiment Interpretation
Run experiments to compare prompts, models, or pipelines. Measure metrics like accuracy and speed to quantify progress.
RAG Observability & Evals
Specialized metrics for retrieval-augmented generation pipelines including retrieval relevance, answer faithfulness, and context completeness.
Advanced Topics
Extend your evaluation approach for complex, multi-step applications.
Evaluating Multi-Turn Conversations
Evaluate coherence, memory, and resolution in conversational apps that maintain context across multiple turns.
Simulated Multi-Turn Conversations
Generate and test user-AI exchanges safely before production. Build realistic dialogue scenarios for robust evaluation.
Agent Evaluation
Assess end-to-end trajectories for agents with tool use and planning. Evaluate action selection and task completion.
Vibe Coding a Custom Annotation UI
Build a custom annotation UI in minutes using Neural Inverse as data source
Automatic Prompt Improvement with AI Agent Skills
Use the Neural Inverse skill to analyze trace feedback and iteratively improve your prompts.
Cookbooks
You can find the ipynb files in the cookbook directory of the repository.
Classification
Evaluation
Examples
Integrations
Prompt Management
Security
Other
Video Tutorials
Beginner's Guide to RAG Evaluation with Neural Inverse and Ragas
External Evaluation Pipelines
Introducing Datasets v2
Introducing Neural Inverse 2.0
Introducing the observe() decorator for Python
LLM-as-a-Judge Evaluators for Dataset Experiments
LLM Playground
Posthog Integration
Run Neural Inverse Locally in 3 Minutes
Webinar: Traceability and Observability in Multi-Step LLM Systems
