Large Language Models
llm
AI Engineering
Correct common LLM assumptions
Explain why a context window is not a word count, why a model does not remember earlier chats on its own, and why temperature zero does not make an answer true.
Diagnose incomplete model output
Work out why a model's answer looks cut off or incomplete by reading termination metadata, token budgets, and stream evidence before changing any setting.
Choose long context or retrieval
Compare sending whole documents in a large context window with retrieving a few passages for two features of one product, using window fit, cost with prefix caching, latency, access, and a sliced pilot, and say what would change each choice.
Select a model under quality, cost, and latency limits
Recommend a model or routing policy from measured quality, cost, and latency, say what evidence is still missing, and revise the decision when the traffic mix changes.
Write an extraction prompt with examples
Rewrite a vague request into a testable extraction prompt with category rules, a missing-data policy, consistent examples, and a JSON output contract, then explain why parseable JSON can still be wrong.