RAG & Knowledge
Giving a model your data — retrieval, fine-tuning, context.
Chunking strategies for RAG that actually work
Chunking decides what your retriever can even find. The strategies that hold up, the size-and-overlap tradeoffs, and how to test chunking against real queries.
Embeddings explained, without the heavy math
What embeddings actually are, the geometry intuition behind them, how similarity search works, what they power, how to pick a model, and when to skip them.
Entity SEO for the AI Era: How to Become a Brand AI Actually Knows
AI recommends brands it recognizes as real and notable. Entity SEO is how you become one — consistent identity, schema, knowledge-graph presence, and corroboration that compounds over years.
How AI Decides Which Brand to Recommend
The four things that actually decide whether ChatGPT, Claude, or Perplexity names your brand instead of a competitor — retrieval, extractability, consensus, and entity trust.
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.