A Tensor API Lab collection
An LLM workflow needs a clear contract around the model. This collection follows requests from tokenization through inference, usage records, validation and evaluation, with attention to the decisions an application team can actually control.
Use these guides when a prototype has become difficult to explain: output length varies, a batch contains uneven sequences, or a classifier produces a plausible label without enough evidence. The articles separate model behavior from application policy. That separation helps you decide where a check belongs and what should happen when it fails.
Begin with the inference walkthrough for a shared vocabulary, then choose the guide that matches your bottleneck. Token budgeting focuses on measurable usage and capacity; classification focuses on the meaning and cost of a decision. Keep your evaluation examples versioned alongside the contract. A change is easier to assess when the team can compare the same task, data and acceptance criteria.
3 articles in this collection
Make 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 readTurn classifier outputs into decisions that can be evaluated. Define label semantics, distinguish scores from calibrated probabilities, choose review thresholds, and measure precision, recall, coverage, and failures across realistic inputs.
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
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