Follow your curiosity.
Use topic tags to follow a specific question through the API Depot Field Notes. Each topic connects a short explanation with relevant articles and a route back to the broader integration workflow.
AI APIs
AI APIs expose model capabilities through a service interface. Evaluate the task, supported inputs, output expectations, usage rules, and operational tradeoffs together when choosing how they fit an application.
Explore the topicAI Operations
AI operations connects model behavior with monitoring, incident handling, data practices, and release decisions. Observe quality and workload as well as request success to understand how the application behaves over time.
Explore the topicAPI Discovery
Finding candidate services begins with a concrete capability and a clear understanding of the information needed to choose between them. API discovery links categories and provider descriptions with the real task your application needs to perform.
Explore the topicAPI Evaluation
API evaluation makes the selection criteria explicit. Compare representative results, documentation quality, authentication, operating constraints, and the work needed to maintain the integration beyond a first demonstration.
Explore the topicAPI Security
API security connects credential handling, authorization, input validation, and operational controls. Begin with the provider’s documented access model and a clear boundary between application data and external services.
Explore the topicAuthentication
Authentication establishes the identity or credential associated with a request. The application must also respect authorization: the actions and data that the resulting identity is permitted to access.
Explore the topicCost Planning
Cost planning starts with a defined unit of work and the usage associated with it. Separate assumptions from measured behavior, and track how inputs, outputs, retries, and background jobs affect the total workload.
Explore the topicEmbeddings
Embeddings represent content as numeric vectors for tasks such as similarity and retrieval. Their usefulness depends on the representation, the comparison method, and how the resulting candidates support the application’s real task.
Explore the topicEvaluation
Evaluation compares actual behavior with a defined standard. For AI workflows, use representative examples and explicit quality criteria; for API selection, keep task fit and operational constraints visible beside the demonstration.
Explore the topicIntegration
Integration connects a provider’s request contract with the behavior of your application. It includes the input, authentication, response handling, and operational rules that make a service useful inside a larger workflow.
Explore the topicLLM Tokens
LLM tokens are units used when model inputs and outputs are represented and processed. Understanding token usage helps you reason about context capacity, response length, and the usage components of a service bill.
Explore the topicMonitoring
Monitoring gives a team information about the behavior of a running integration. Choose signals that reflect its task, establish useful baselines, and define who should act when the observed behavior changes.
Explore the topicPrompt Design
Prompt design specifies the task, context, boundaries, and expected response from a model. Treat the instructions as a versioned part of the application and evaluate the effect of changes on representative examples.
Explore the topicREST APIs
REST-style interfaces commonly organize interactions around resources and HTTP requests. Understanding the documented method, path, headers, request data, and response contract helps you make a reliable first integration.
Explore the topicRate Limits
Rate limits control the amount or frequency of activity a provider accepts. Treat the documented limits and error responses as part of the integration contract, and decide how your application should delay or decline additional work.
Explore the topicReliability
Reliability concerns how an integration behaves across expected success and foreseeable failure. Define deadlines, recovery decisions, and observable signals for the workflow rather than assuming that every request completes normally.
Explore the topicVector Search
Vector search retrieves candidates by comparing representations in a vector space. Plan the indexing, retrieval, evaluation, and content-access boundaries together before relying on a match as evidence for an answer.
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