OpenAI
openai
Behavioral Interviews
OpenAI: Mission Alignment and Safety
OpenAI's behavioral loop sits at the intersection of three signals that no other major engineering employer asks for in the same combination: substantive engagement with the AGI mission, serious consideration of safety as a daily constraint, and the intensity of frontier-lab work paired with collaborative care. Candidates who walk in with strong engineering credentials but no view on the mission, or who recite mission language without engaging with the safety-versus-capabilities tension, do not score well. This lesson defines what mission alignment actually means in interview context, walks through how the loop probes safety thinking specifically, maps the cultural signals to the questions interviewers ask, and shows two model answers tailored to the mission-articulation and intensity-with-care signals OpenAI privileges.
Community
Streaming LLM Response Consumer With Cancel
When a user navigates away mid-completion we still get billed for the remaining tokens. This is the SSE-style consumer I wrote that decodes JSON deltas, exposes a `cancel()` that aborts the request, and never leaks a reader on errors.
Building RAG: The Pipeline and Its Failure Modes
The full RAG pipeline (ingest, chunk, embed, retrieve, generate, evaluate), the seven failure modes I have actually hit, and the eval discipline that has kept my retrieval-augmented features honest in production.
LLM Glue Questions From an AI Platform Loop
Four questions a senior engineer at an AI platform team asked me in 2024. Streaming token forwarding, retry-and-fallback across providers, tool-call validation, and a small token budgeter.
Prompt Engineering Patterns That Survived Six Months of Prod
The five prompting techniques that have actually held up across model upgrades, the four that I tried and dropped, and the eval discipline that lets me tell which is which.
A Prompt Template With Safe Interpolation
After a customer email leaked into a system prompt and changed the model's persona, I built a 30-line template that quotes user input, fences code, and refuses unknown placeholders. Use it before every LLM call.
LLM Fundamentals: Tokens, Context, and Cost
Tokens are not characters or words. Context is not free. Cost is per-token in both directions. The three fundamentals that determine 80% of how an LLM-backed feature performs and bills.
Embeddings and Vector Search, Explained for Devs
What an embedding actually is, why cosine similarity is the metric you reach for, and the production decisions (chunking, hybrid search, dimension count) that determine whether a vector search ships or sits.