AI Tool ConsultantAI engineering
RAG, guardrails, grounding, evaluation and launching AI features, explained with real builds instead of theory.
AI engineering
The AI Builder's Glossary: Agents, Skills, MCP, RAG and Evals in Plain English
The AI field in five themes and 40 terms, in plain English: models, context and RAG, tools and agents, reliability, and shipping AI features.
AI engineering
RAG Explained With a Real Example: The Phrase Bank Behind a Language App
RAG explained through a real pipeline: a phrase bank built from class notes, embeddings, vector search, a grounding check and quality tests.
AI engineering
Prompt Injection Defense in Six Layers, From a Real Language App
No single prompt stops prompt injection. Six stacked layers from a real language app, tested with real calls, and the risk that remains.
AI engineering
Grounding an LLM in Official Facts: How to Stop AI Feedback From Inventing Things
Make an LLM check answers only against official facts you supply, never its memory. A worked example with tests, and why code gets the last word.
AI engineering
How to Evaluate LLM Output Without Fooling Yourself
Seven evaluation rules from 55+ auditable LLM runs with zero retracted numbers: re-derive numbers, check confounds, compare on the same items.
AI engineering
Speech-to-Text for Language Learners: Why Transcription Hides Your Mistakes
Most transcription quietly repairs a learner's grammar. The Whisper settings that keep mistakes, how to stop silence hallucinations, and a test.
AI engineering
Before You Launch an AI Feature: Budgets, LLM Rate Limiting and Consent
Before an AI feature goes public: budgets at three levels, rate limits counted in a database, handling 429s, consent for analytics, and rollback.