RAG & Knowledge
Giving a model your data — retrieval, fine-tuning, context.
Building With AI: Frequently Asked Questions
Practical answers for builders: model choice, RAG vs fine-tuning, agents, hallucinations, evals, cost, latency, and getting started with an LLM.
Context Engineering: The Skill That Replaced Prompt Hacking
Managing the context window is the real craft now. What to put in, retrieval vs stuffing, ordering, caching, compaction, token budgets, and multi-turn memory.
RAG vs Fine-Tuning vs Long Context: How to Give a Model Your Knowledge
Three ways to put your proprietary knowledge into an LLM — retrieval, fine-tuning, long context. What each costs, when each wins, how they combine.
Vector Search vs Keyword Search for RAG
Semantic embedding retrieval vs lexical keyword search for RAG — accuracy, cost, setup, failure modes, and why hybrid usually wins.
When Fine-Tuning Is Actually Worth It
The honest cases for fine-tuning versus prompting, RAG, and long context — plus the maintenance cost that's why most teams shouldn't start here.