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The 5-minute loop
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An interactive look at what your daily Poonami session will feel like. Switch agents, answer the quiz, step through the lesson.
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Builder Agent · Monday, March 30
RAG vs Fine-tuning: When to use each
Concept
RAG (Retrieval-Augmented Generation) pulls external data at query time. Fine-tuning bakes knowledge into model weights. RAG is cheaper and stays current; fine-tuning is faster at inference but expensive to update.
Agent takes
News Agent
The RAG vs fine-tuning debate is heating up as retrieval systems get faster. Watch this space.
Research Agent
Academic literature shows RAG reduces hallucination by ~34% on knowledge-heavy tasks.
Career Agent
Knowing when to use RAG vs fine-tuning is one of the top AI interview questions right now.
Coach Agent Quiz
When should you choose RAG over fine-tuning?
Lesson anatomy
Key concept
Real example
Tool / workflow
Quiz
Action task
Your progress
Top 23% of learners this week
Meta releases Llama 4 — biggest open-source model yet
Signal
Open weights shift power back to developers. Self-host frontier intelligence with zero API costs.
# your prompt
Summarise this PDF and extract the 3 key action items
→ RAG pipeline · LlamaIndex
Zero code required. Works in 5 minutes.
Flashcard · Day 3
What is RAG?
Retrieval-Augmented Generation — pulls live data at query time. No retraining needed.
5 day streak · 90% retention rate
How to land an AI role without an ML degree
Ship 3 AI projects publicly
Document your workflows
Rewrite your LinkedIn summary
Today's action
Write your AI professional summary →
Benchmark breakdown: what the scores actually mean
MMLU benchmark · Third-party data
AI will take your job — and that might be fine
For
Technology always creates new jobs. History proves it.
Against
Not for the same people, or in the same timeframe.
Form your own view. Both sides, every time.
Your progress
Week 35
Day streak
5 of 7 agents completed today
What is the primary advantage of open-weight models?
Correct! +10 streak points