Follow the thread
Follow a language-model request from input representation to usable output.
Inference articles connect token IDs, tensors, model results and application policy. They are useful when the boundary between what the model computes and what the application returns has become unclear. Choose the walkthrough for the conceptual path, then the provider guide for a particular integration context.
As you read, record which details are observable through your chosen interface and which remain internal. Keep those two lists separate when designing logs or promising behavior to callers. A response that is valid at the transport layer still needs task-specific checks before it becomes a reliable application decision.
4 articles tagged LLM inference
Trace text from vocabulary tokens to embedding vectors and shaped tensors. Understand how masks, pooling and model versions affect API design, memory estimates and reliable similarity search. Keep representation size separate from the meaning of its values.
7 min readMake LLM consumption predictable with a budget for each task. Separate context limits from output reserves, choose batching and cache policies, and connect estimates, retries, and reported usage to useful completion.
7 min readLearn where numerical processing belongs around an Anthropic Messages integration, then design clear application contracts, traceable evidence, validation and evaluation without assuming access to model internals. Keep provider metadata separate from model-generated conclusions.
7 min readFollow the transformations between a prompt and a useful response. Understand token IDs, attention masks, model-output shapes, decoding choices, and the metadata that makes an LLM integration easier to debug and evaluate.
7 min read
TensorAPI.com
