AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Token optimization, prompt compression, and context trimming
Token optimization removes unnecessary model input/output while preserving task information. Prompt compression shortens instructions or evidence; context trimming selects only relevant history and sources. Shorter is useful only if important conditions…
Small versus large models and practical routing
Smaller models often reduce cost/latency but may perform worse on complex tasks; stronger models may justify higher cost for difficult reasoning or synthesis. Routing sends different tasks to appropriate paths. The right split comes from evaluation, not model…
Exact caching, prompt caching, and semantic caching
Exact caching reuses results for the same normalized request and dependencies. Provider prompt caching can reduce processing/billing for supported repeated prefixes. Semantic caching reuses results for similar meanings, which is more risky because similar…
Batch processing, concurrency, rate limits, streaming, and fallback
Batch processing groups noninteractive tasks; bounded concurrency controls simultaneous requests; rate limits constrain traffic over time. Streaming exposes progress earlier. Fallback uses an approved alternative after eligible failures.
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.