Guide to building your first LLM-powered app
  • Building an LLM-powered Application
    • Who this guide is for
  • Before you build
    • As a Business User
    • As a Product Manager
    • As a Designer
  • Implementing LLMs
    • Model Selection
    • Prompt Engineering
    • Model Failures
  • Important Next Steps
    • On LLM Usage Analytics
    • On LLM Fraud, Waste & Abuse
    • On Pricing LLM-powered features
    • Beyond V1
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Building an LLM-powered Application

NextWho this guide is for

Last updated 1 year ago

LogicLoop is a business alerting platform that lets users write SQL across their data to trigger alerts and automations. We recently added an , and jotted down some hard-learned observations that others implementing LLM-enabled features might find useful, primarily around:

  • What to know before you start building

  • What we learned implementing LLMs

  • Important next steps that don't get talked about enough

Since this guide is influenced by our specific experiences, here’s some context about how we integrated LLMs into our SaaS applications:

LogicLoop AI SQL Copilot helps you write queries like “find me the top 10 customers that have opened my email in the last 30 days”, explain and fix SQL queries. We also have a chatbot-like interface to help you answer more open-ended data questions.

LogicLoop AI SQL Copilot Demo
AI SQL Copilot
LogicLoop AI SQL Copilot Promo - Watch Video