llm-pipeline-design

Name: llm-pipeline-design
Availability: InStock
Author: jota-batuta

Design end-to-end LLM pipelines with Langfuse tracing and auto-supervision.

Updated Feb 20, 2026

Search Tags:#design #llm #observability #pipeline #drift-detection #prompt-management

Ready to run

npx skills add https://github.com/jota-batuta/batuta-dots --skill llm-pipeline-design

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-pipeline-design
Source: https://github.com/jota-batuta/batuta-dots/tree/main/BatutaClaude/skills/llm-pipeline-design
Command: npx skills add https://github.com/jota-batuta/batuta-dots --skill llm-pipeline-design

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about llm-pipeline-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design an observable LLM pipeline with drift detection and prompt management?▼

To design an observable LLM pipeline, you integrate client data, auto-supervise models, and enable full observability with Langfuse. This enforces versioned prompts, confidence scoring, tracing, PII redaction, and drift detection for auditable governance.

What is LLM pipeline calibration and why is auto-supervision needed?▼

LLM pipeline calibration is the process of statistically analyzing data to route models and score confidence accurately. Auto-supervision is needed to reduce guesswork, detect drift, and improve enterprise AI governance across classification and evaluation tasks.

Can I use Langfuse tracing for model routing pipelines that direct cases to different capacity models?▼

Yes, you can build a routing pipeline that directs simple cases to fast models and complex ones to higher-capability models. Langfuse tracing provides full observability across this routing workflow while enforcing PII redaction.

What's the best way to ensure PII redaction and auditable governance in LLM pipelines?▼

The best way to ensure PII redaction and auditable governance is to enforce requirements for versioned prompts, confidence scoring, and tracing via Langfuse. This end-to-end pipeline design integrates data extraction and statistical analysis for compliance.

Do I need statistical analysis and data extraction before model routing in an LLM pipeline?▼

Yes, data extraction and statistical analysis are required workflow stages before model routing. Designing calibrated LLM pipelines starts with ingesting real client data to inform auto-supervision, confidence scoring, and drift detection accurately.