LLM API USE CASES
Start with a concrete workload, not a generic model ranking. Each guide defines the inputs, workflow, failure cases, and metrics needed to choose a route for that job.
Choose a starting point
01Choose and evaluate an LLM API for generating application code, tests, and small features from written requirements.
02Evaluate LLM APIs for pull-request review, bug detection, security checks, and actionable inline feedback.
03Plan and validate multi-file refactors that must preserve behavior, public APIs, tests, and architecture boundaries.
04Design a support chatbot around grounded answers, escalation rules, response time, and cost per resolved conversation.
05Evaluate generation, retrieval, citations, context limits, and abstention for retrieval-augmented chatbots.
06Compare multilingual chatbot APIs on language coverage, intent preservation, locale rules, safety, and latency.
07Test function-calling APIs for tool selection, argument accuracy, schema compliance, retries, and safe execution.
08Evaluate browser agents on page understanding, action selection, recovery, confirmation, and complete-task cost.
09Build reliable multi-step automation with explicit state, bounded retries, idempotency, approvals, and audit logs.
10Compare LLM APIs for long-document extraction, question answering, comparison, and evidence-linked summaries.
11Evaluate schema-constrained extraction from documents, emails, tickets, and other unstructured text.
12Compare math reasoning APIs with answer verification, tool use, consistency checks, latency, and full-solution cost.
How to use this section
Coding, chatbots, agents, and reasoning fail in different ways. Open a task guide when you know the workload, or use the family pages to compare adjacent tasks.
These are not model-name landing pages. They explain how to implement and evaluate code review, RAG, function calling, structured extraction, and other concrete workloads, then connect the decision to current routes and pricing.
It is a bounded workload with specific inputs, outputs, failure modes, and pass criteria, such as reviewing a pull request or answering from a private knowledge base.
Shortlist routes with the required capabilities, then run the same representative tasks and compare pass rate, latency, retries, and cost per completed task.
Possibly, but do not assume it. A route that works for chat may not be the most reliable or economical choice for tools, extraction, or repository-scale coding.
Choose one task guide, build its evaluation set, and run the same cases on two current routes.
