How AI fits design and front-end work
How AI fits design and front-end work when a person still makes the decisions.
- What Is Retrieval-Augmented Generation?RAG grounds large language models in your own content so answers stay accurate, current, and on-brand. Here is how the pattern works and when to reach for it.
- Prompt Engineering Fundamentals for TeamsGood prompts are specifications, not incantations. Learn the structural habits that make model output predictable enough to build on.
- Choosing an LLM for Your ProductModel selection is a trade-off between capability, latency, and cost. A practical framework for picking the right model tier for each job.
- AI Agents in Production: What Actually WorksAgentic systems promise autonomy but fail in new ways. Patterns for scoping, guardrails, and human oversight that survive contact with real users.
- Fine-Tuning vs. Prompting: When Each WinsMost teams reach for fine-tuning too early. A decision guide for choosing between prompting, RAG, and fine-tuning based on what you are actually trying to change.
- AI Content Workflows for Small TeamsHow lean marketing and product teams use AI to multiply output without publishing generic sludge. A workflow built on briefs, drafts, and human editing.
- Understanding Embeddings and Vector SearchEmbeddings turn meaning into geometry, and they power search, recommendations, and RAG. A plain-language tour of how they work and how to use them well.
- Evaluating LLM Output QualityYou cannot improve what you do not measure. How to build lightweight evals that catch regressions before your users find them.
- AI Chatbots for Customer Support Done RightSupport bots earn trust by resolving issues and knowing when to hand off. A blueprint for grounding, escalation, and measuring deflection honestly.
- Responsible AI Practices for Client WorkUsing AI in agency and consulting engagements raises questions about disclosure, data handling, and quality accountability. Practical policies that protect everyone.