Ideas, made tangible
Ten original deep dives into tensors, tokens, LLM workflows and the decisions around them. Start with the basics or pick the problem you are working through.
10 articles · All articles
Build traceable AI data workflows for tokenized assets by preserving source identity, observation times and derivation records. Separate claims from conclusions and evaluate classification with realistic evidence gaps. Connect each model result to the records that supported it.
7 min readTrace 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 readUse Cursor to develop inference integrations with focused files, durable project rules and testable tasks. Learn how adapter boundaries, realistic fixtures and human review turn generated changes into maintainable code. Finish with a clear implementation handoff.
7 min readConnect prompt design to an explicit output contract. Use schemas, unavailable states, source-reference checks, bounded corrections, and focused evaluation cases to turn generated text into records that an application can validate.
7 min readChoose the right Azure endpoint for an inference workload, define numerical data contracts and keep preprocessing, model versions, batch recovery and rollout decisions traceable across the full pipeline. Cover both interactive requests and larger asynchronous classification jobs.
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 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 readA tensor contract needs more than an array of numbers. Learn how to name axes, validate numerical types, preserve feature meaning, and batch compatible examples without hiding errors at the API boundary.
7 min read
TensorAPI.com
