llm-pipeline-design
CommunityDesign calibrated LLM pipelines with real data.
Authorjota-batuta
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Designs end-to-end LLM pipelines that are data-driven, observable, and auto-supervised for enterprise AI applications, reducing guesswork and improving governance.
Core Features & Use Cases
- End-to-end LLM pipeline design including data ingestion, statistical analysis, model routing, auto-supervision, confidence scoring, and drift detection.
- Applicable to classification, evaluation, prompt management, and governance tasks across client projects.
- Real-world example: Build a routing pipeline that directs simple cases to fast models and complex ones to higher-capability models, with Langfuse tracing and PII redaction.
Quick Start
Design and deploy an end-to-end LLM pipeline for a client project, ensuring data ingestion, model routing, auto-supervision, and Langfuse tracing.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: llm-pipeline-design Download link: https://github.com/jota-batuta/batuta-dots/archive/main.zip#llm-pipeline-design Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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