Handling PII in LLM Pipelines: Redaction, Retention, and Access Control
Quick answer: send the model as little personal data as the task needs, redact or replace identifiers before they leave your systems, keep them out of logs,...
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AI
Guardrails for LLM Output: Validation, Moderation, and Fallbacks
Quick answer: guardrails are checks around a model that catch bad inputs and bad outputs before they reach users or systems. Layer cheap deterministic checks,...
Evaluating LLM Features With Offline Test Sets and Online Metrics
Quick answer: evaluate LLM features in two stages. Offline, run a curated test set against every candidate change and grade results with programmatic checks,...
LLM Cost Control With Prompt Caching and Output Length Limits
Quick answer: LLM cost is mostly tokens multiplied by price, so reduce both. Measure cost per feature first, then trim prompts, retrieve fewer and better...
Human-in-the-Loop Design for AI Agent Actions With Side Effects
Quick answer: let an AI agent act freely on read-only and easily reversible tasks, and require human approval for actions that send messages, move money,...
LLM Observability: What to Log and Trace in Production AI Features
Quick answer: log every LLM call as a trace with the prompt version, model and parameters, redacted inputs and outputs, retrieved document IDs, tool calls,...
Fine-Tuning vs RAG vs Prompting: Choosing an Approach for Domain Knowledge
Quick answer: start with prompting, because it is the cheapest to try. Use retrieval-augmented generation when answers depend on private, fresh, or frequently...
Dedicated Vector Stores vs Relational Databases With Vector Search
Quick answer: if your data already lives in a relational database and your vector count is modest, adding vector search there is usually simpler and keeps data...
LLM Rate Limit Handling: Retries, Backoff, and Queueing at Scale
Quick answer: LLM providers limit both requests and tokens per minute, and exceeding them returns rate limit errors. Retry only retryable errors, use...