Measure the task. Not just the API call.
Connect quality, latency, retries, and resource use to the outcome your user actually needs.
Read the guideNeural Notes
A field guide for
your next good question.
Original, practical reads on the interfaces connecting models to the real world. Less mystique. More useful detail.
Tool calling without handing over the keys.
Give agents useful tools while keeping permissions, approvals, and execution in your application.
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AI trading research needs evidence, not promises.
Keep point-in-time data, reproducible experiments, and hypothetical results separate from execution.
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When language meets motion, boundaries matter.
Separate language-based task proposals from the controller that owns physical execution.
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Build chat memory that knows its boundaries.
Separate session context, durable memory, and retrieved knowledge without losing user control.
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Structured outputs: valid JSON is just the beginning.
Design output contracts that represent uncertainty, preserve evidence, and survive real inputs.
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Give your LLM the right context with RAG.
Build a retrieval pipeline you can inspect, from document preparation to evidence-backed answers.
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Brain neural APIs: measurements before meaning.
Preserve channels, timing, and provenance before interpreting research biosignal data.
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Prompt. Test. Repeat. A better template workflow.
Treat prompt changes as testable releases, with clear inputs, useful examples, and a rollback path.
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What is a neural API? Start with the interface.
Models, interfaces, and the decisions that turn an interesting experiment into a reviewable application.
Read the guideRead by the boundary you are designing.
Follow the Foundations collection to define a task, Builders to turn it into a contract, Applied AI to explore higher-consequence settings, or Operations to measure the completed workflow. Each guide includes a primary source and a next step. The topic archives below collect the same ideas across fields.