DRAPER EP 4mp4

Buckshotio · Intermediate ·🧠 Large Language Models ·9mo ago

About this lesson

Building DRAPER | ep 8 This series shares the process behind developing DRAPER — covering its concept, tools, and early steps toward helping brands build their identities. –– The first version of DRAPER was built inside ChatGPT as a custom GPT. It worked, but the tool still drew from a wide knowledge base. We decided to move off-platform sooner than expected. Building on the API gave us more control over workflows, structured prompts, and larger inputs. This shift also created new requirements — a front end and database were needed, raising new technical questions about how to scale. Follow for more. #DraperAI #BehindTheBuild #AIProduct #StartupJourney #DesignProcess

Original Description

Building DRAPER | ep 8 This series shares the process behind developing DRAPER — covering its concept, tools, and early steps toward helping brands build their identities. –– The first version of DRAPER was built inside ChatGPT as a custom GPT. It worked, but the tool still drew from a wide knowledge base. We decided to move off-platform sooner than expected. Building on the API gave us more control over workflows, structured prompts, and larger inputs. This shift also created new requirements — a front end and database were needed, raising new technical questions about how to scale. Follow for more. #DraperAI #BehindTheBuild #AIProduct #StartupJourney #DesignProcess
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
OWASP LLM05:2025 : Improper Output Handling — When the Model’s Response Becomes the Attack
Learn about OWASP LLM05:2025, a security risk related to improper output handling in LLMs, and how to mitigate it
Medium · LLM
 📰
AI and UX Opinion — Episode 006
Learn about the current state of AI and UX design, focusing on prompts, and why it matters for designing effective user experiences
Medium · UX Design
 📰
The LLM Thought a Dollar Was Still ₦450: Building a Car Pricing Engine for a Market With No Data
Learn how to build a car pricing engine using LLMs for a market with no data, and understand the challenges of working with outdated currency exchange rates
Dev.to · Chichebe John
 📰
Cross-Modal Knowledge Distillation for precision oncology clinical workflows under multi-jurisdictional compliance
Apply cross-modal knowledge distillation to improve precision oncology clinical workflows while ensuring multi-jurisdictional compliance
Dev.to AI
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →