Blog
Practical notes on RAG, LLM evaluation, analytics, prompt design, and applied AI products. Written for fast understanding, not buzzwords.
RAG / LLMs / Document AI
A clear story about retrieval augmented generation, why it matters, and how it changes a chatbot from guessing to looking things up.
Core idea
RAG is not magic. It is a disciplined way to let an AI answer from the right documents instead of relying only on memory.
A practical decision story for builders choosing between training a model and giving the model better context.
Start with RAG when the problem depends on fresh documents. Consider fine-tuning later when the behavior itself needs to change.
A build story about turning dense legal documents into a usable RAG workflow with citations, risk flags, and careful output design.
The hardest part of legal AI is not calling a model. It is making the answer inspectable, cautious, and useful.
Lessons from building a transaction analytics app that turns CSVs into forecasts, anomalies, charts, and reports.
AI analytics works best when the interface first makes the data structure obvious.
A grounded explanation of prompt engineering as task design, constraints, examples, evaluation, and output structure.
A prompt is good when it makes the task testable, repeatable, and easy to evaluate.
A friendly story for understanding embeddings, semantic search, and why vector databases matter in RAG apps.
Vector search helps an app find related meaning, even when the user's words do not exactly match the document.
A practical view of why live AI demos, case studies, and code links communicate engineering ability better than long stacks of badges.
A working project shows judgment: what problem was chosen, how the workflow behaves, and whether the result can be inspected.
A clear explanation of the signals that matter in applied AI engineering: working systems, product thinking, evaluation, and communication.
Applied AI teams need builders who can turn models into reliable workflows people can actually use.
