Primer

How to avoid AI slop? AI that actually works is built differently. These chapters are the difference between shipping something reliable and shipping something that looks right once.

by Nishchay Vishwanath · built while learning
Chapters
01
AI Evals
You can't tell if it works by looking at it. Evals are how you know — six concepts, six live components, from graders to pass@k.
Live →
02
Feedback Loops
Your agent retried and failed again. Without a feedback loop, retrying is just hoping — here's what iteration actually means.
Live →
03
Agentic System Design
Your multi-agent system ran to completion and returned a confident wrong answer. Silent failures are the default — here's how to design systems that fail visibly.
Live →
04
Agent Harness Engineering
It looked done in 20 minutes. It didn't work. The harness around the model — not a smarter one — is what closes that gap.
Live →
05
RAG
Hallucinations are a retrieval problem. Grounding your AI in real data is how you fix it.
Soon
06
Fine-tuning
Generic model, generic output. Fine-tuning is how you make AI care about your specific problem.
Soon
Also here
Agent Learning Roadmap
A personal tracker — wedge strategy, Claude Code feature adoption, and an LLM-production curriculum.
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Primer — interactive AI explainers nishchay.me ↗