Novacademy Blog
Guides for building with AI
Plain-English explainers and practical walkthroughs on RAG, agents, evals, and the engineering judgment to ship real AI features.
Context Windows, Explained Without the Jargon
Tokens, context windows, and why an AI 'forgets' things — explained in plain English with one analogy that makes the rest obvious.
RAG, Explained by What Actually Breaks in Production
Naive RAG demos beautifully and fails in production. Here are the three things that break it — chunking, retrieval, and context bloat — and the mental model that fixes them.
Prompt Engineering: How to Write Prompts That Actually Work
A practical guide to prompt engineering — the core techniques for getting reliable, high-quality outputs from LLMs without changing the model at all.
Fine-Tuning vs. RAG: How to Choose the Right Approach
Fine-tuning and RAG both make LLMs more useful — but they solve different problems. Here's how to think through the choice for your use case.
AI Agents vs. Workflows: When to Use Which
Not every LLM feature needs an agent. A practical look at the difference between agents and workflows — and how to choose the right one for your task.
What Is RAG? Retrieval-Augmented Generation, Explained
A plain-English guide to retrieval-augmented generation — what RAG is, why it beats fine-tuning for most use cases, and how the pieces fit together.