What are the key takeaways from “How I'd Learn AI From Scratch in 2026 (skip the useless 80%)” on Jeff Su?
Insights from the Jeff Su episode “How I'd Learn AI From Scratch in 2026 (skip the useless 80%)”, published June 9, 2026.
Frequently asked questions about “How I'd Learn AI From Scratch in 2026 (skip the useless 80%)”
What is "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)" about?
In "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)" (Jeff Su, June 2026), the era of complex prompting is ending. The most effective way to use AI today is to provide rich, curated context through examples and projects, allowing models to infer your needs. This shifts the focus from writing clever…
What does "Outcome + Context (OC) Framework" mean in "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)"?
In "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)", Instead of describing a role or format in a prompt, you provide an example of successful work. The AI uses this as a template, automatically inferring the necessary tone and structure, which is significantly more accurate than manual instructions.
What does "AI Projects" mean in "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)"?
In "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)", Projects act as a 'permanent home' for recurring tasks. They house project instructions (rules), knowledge files (reference data), and memory (updates), ensuring the AI doesn't start from scratch every time you open a new chat.
What does "AI Systems" mean in "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)"?
In "How I'd Learn AI From Scratch in 2026 (skip the useless 80%)", While projects are silos, an AI system functions as a higher-order layer that sees the 'big picture.' It can flag patterns (e.g., financial constraints affecting travel plans) that individual projects would miss.
What is this episode about?
The era of complex prompting is ending. The most effective way to use AI today is to provide rich, curated context through examples and projects, allowing models to infer your needs. This shifts the focus from writing clever prompts to building systems that compound your data over time.
What are the key takeaways?
Prioritize paid tiers of AI models, as the performance gap compared to free versions is substantial for professional work. — Using a more powerful model allows the AI to break down complex tasks and catch nuances automatically.
Stop searching for 'perfect prompts' and start providing 'examples of what good looks like'. — Examples encode implicit information like tone and team preferences that are difficult to describe in text.
Use .md markdown files instead of PDFs when uploading context to AI projects. — Markdown files are significantly faster for models to process and more accurate for them to read.
What concepts are explained?
Outcome + Context (OC) Framework: Instead of describing a role or format in a prompt, you provide an example of successful work. The AI uses this as a template, automatically inferring the necessary tone and structure, which is significantly more accurate than manual instructions.
AI Projects: Projects act as a 'permanent home' for recurring tasks. They house project instructions (rules), knowledge files (reference data), and memory (updates), ensuring the AI doesn't start from scratch every time you open a new chat.
AI Systems: While projects are silos, an AI system functions as a higher-order layer that sees the 'big picture.' It can flag patterns (e.g., financial constraints affecting travel plans) that individual projects would miss.
Reconciliation (AI Feedback Loop): By comparing the AI's initial output with your final, edited version, you teach the model to internalize your preferences, which causes the AI's future outputs to require significantly less editing.
