AI
10 articles — pick one and start reading.
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.
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Prompt Engineering Fundamentals for Teams
Good prompts are specifications, not incantations. Learn the structural habits that make model output predictable enough to build on.
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Choosing an LLM for Your Product
Model selection is a trade-off between capability, latency, and cost. A practical framework for picking the right model tier for each job.
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AI Agents in Production: What Actually Works
Agentic systems promise autonomy but fail in new ways. Patterns for scoping, guardrails, and human oversight that survive contact with real users.
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Fine-Tuning vs. Prompting: When Each Wins
Most 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.
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AI Content Workflows for Small Teams
How lean marketing and product teams use AI to multiply output without publishing generic sludge. A workflow built on briefs, drafts, and human editing.
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Understanding Embeddings and Vector Search
Embeddings 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.
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Evaluating LLM Output Quality
You cannot improve what you do not measure. How to build lightweight evals that catch regressions before your users find them.
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AI Chatbots for Customer Support Done Right
Support bots earn trust by resolving issues and knowing when to hand off. A blueprint for grounding, escalation, and measuring deflection honestly.
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Responsible AI Practices for Client Work
Using AI in agency and consulting engagements raises questions about disclosure, data handling, and quality accountability. Practical policies that protect everyone.
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