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LLM Data & Infrastructure

  • Artificial Intelligence (AI)
  • Data Pipelines
  • Synthetic Data
  • LLM
  • AI Infrastructure
  • Generative AI
  • Data Infrastructure
Building AI applications requires proper data and LLM infrastructure. We've assembled a list of guides on data pipelines, LLM engineering fine-tuning, RAG, and model training.
LLM Data & Infrastructure

LLM Engineering & Data Management

Data Pipelines

  • Article

Why Data Pipelines Are Key

General Partner Jesse Robbins explains how, in the same way software teams reinvented delivery with CI/CD, AI programs need robust pipelines to succeed.

The Data Pipeline is the New Secret Sauce

AI inference infrastructure is fragmented due to cost, compute shortages, performance tradeoffs, and data challenges. Here’s how we find a clearer path forward.

  • Article

How to Create Data Pipelines

This extensive guide discusses how to build data pipelines from first principles, including key concepts and resources to get your project off the ground, as well as to monitor in production.

How to Create Data Pipelines

Learn how to create data pipelines and build them for your use case in this introduction guide.

  • Article

How to Scope and Evolve Data Pipelines

This in-depth interview covers best practices on building data pipelines and evolving them as your organization scales, as well as how to tackle the most common data pipeline challenges.

How to Properly Scope and Evolve Data Pipelines

Data pipeline projects are most successful with a clear plan that considers business metrics. Data expert Stefan Krawczyk explains.

LLM Engineering

  • Article

Guide: LLM Fine-Tuning

Fine-tuning is the most resource-intensive approach to recalibrating a model for better performance. This exhaustive guide covers everything technical teams need to know.

LLM Fine-Tuning: A Guide for Engineering Teams in 2025

What every startup team should know before fine-tuning an LLM—costs, risks, tools, and how to do it right.

  • Article

Training LLMs on Internal Data

This guide covers how to train LLMs using your own internal data, including data processing, model engineering, environment configuration, testing, and iteration.

How to Train an LLM on Your Own Data: Beginner’s Guide

Learn the necessary steps to train an LLM on your own data in this beginner’s guide.

  • Article

How to Use Synthetic Data in AI Programs

This guide offers an overview of synthetic data, an alternative to using proprietary customer data, as well as a full guide to using synthetic data in AI programs.

Synthetic Data for AI: Purpose and Use Cases

This guide covers how synthetic data plays an important role in AI programs, particularly in highly regulated spaces.

Additional Data & LLM Engineering Resources

  • Article

RAG vs. Fine-Tuning

This in-depth guide covers the pros and cons of using retrieval-augmented generation vs. fine-tuning in terms of performance, costs, and regulatory requirements.

RAG vs. Fine-Tuning: What Dev Teams Need to Know

Understand the key differences of RAG vs. fine-tuning, technical trade-offs, and when to use each approach for building LLM-powered applications.

  • Article

Using Synthetic Data in Practice

This expert interview discusses the practical considerations of using synthetic data in AI programs, covering data quality, privacy concerns, and the trade-offs between local and third-party hosting.

The Role of Synthetic Data in AI/ML Programs in Software

Synthetic data provides uniquely important value for software developers, especially in highly regulated spaces. Tonic co-founder Adam Kamor explains.

  • Article

Data Pipelines in Highly Regulated Verticals

This expert interview covers how to manage data pipelines in highly regulated industries like healthcare, from team collaboration to technical processes to fallback plans.

Best Practices for Developing Data Pipelines in Regulated Spaces

Standing up data pipelines in highly regulated spaces requires proper scoping, automation, and storage. Data expert Roshan Nanu explains.

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