One search. Connected ideas.

Beginner — What determines AI cost?

Count every model/tool call, embedding/reranking, infrastructure, retries, and review workload. Optimize cost per successful task subject to quality and privacy constraints. Use context selection, templates, smaller models, caching, batching, and strict agent…

Priority key

Beginner — What makes a good prompt?

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…

Cost formula: input tokens × input rate + output tokens × output rate + tools/retrieval/embedding/infrastructure/review, with applicable caching and provider accounting. Prices must be checked for the real provider/model/account.

An agentic workflow allows a model to choose some steps based on observations. The surrounding application limits the actions, resources, and impact of those choices. It is useful for variable tasks but offers less predictable paths and call counts.

RAG evaluation measures retrieval quality and answer quality independently and together. Fine-tuning changes model behavior through training; long context supplies a large body of content directly; traditional search returns evidence without generating an…

Local or self-hosted models run on infrastructure you control instead of a provider's shared inference API. An API model outsources much of inference operation. Self-hosting can mean a laptop, on-premises servers, or controlled cloud GPU infrastructure.

A strict step/time/token budget stops the run and reports a supported fallback. I inspect progress and repeated calls, compare prompt/tool changes, and track long-tail cost rather than allowing an expensive loop to continue.

Input/output usage, model pricing, tool/search calls, embeddings, retries, and infrastructure. Human review and failed tasks also matter. I calculate total cost per completed useful task rather than only one API call.

Establish a quality/security baseline, measure the expensive stages, and experiment with routing, context reduction, batching, or caching. Release only when held-out outcomes, tail latency, and privacy requirements remain acceptable.

When similar requests differ in tenant, user permissions, policy version, date, or personalized facts. I restrict it to suitable stable tasks and include freshness and scope dependencies; exact caches also require proper keys and invalidation.

Count every model/tool call, embedding/reranking, infrastructure, retries, and review workload. Optimize cost per successful task subject to quality and privacy constraints. Use context selection, templates, smaller models, caching, batching, and strict agent…

I measure which examples actually help and remove redundancy. I test a smaller prompt, provider-supported schemas, deterministic preprocessing, or a better model. I optimize latency and cost while preserving held-out quality.

Beginner — What determines AI cost?

Priority key

Beginner — What makes a good prompt?

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…

Cost formula: input tokens × input rate + output tokens × output rate + tools/retrieval/embedding/infrastructure/review, with applicable caching and provider accounting. Prices must be checked for the real provider/model/account.

An agentic workflow allows a model to choose some steps based on observations. The surrounding application limits the actions, resources, and impact of those choices. It is useful for variable tasks but offers less predictable paths and call counts.

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module