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Gateway retries, streaming segments, and vendor callbacks create duplicate or late usage. This article designs immutable usage events, unique dedup keys, conflict isolation, billing-period watermarks, and three-way reconciliation.
A larger context window doesn't mean you should hand the whole repo to the model. This article covers task-level token budgets, layered summaries, evidence references, cache invalidation, and compression-quality checks to keep a coding agent's engineering judgment intact within limited context.
When a coding Agent takes over a large repository, the biggest waste is indiscriminate file reading. This article designs a repository map, module boundaries, command evidence, and risk labels so the Agent establishes a verifiable working context before changing code.
"Tuning RAG without an eval set is just guesswork." This guide uses RAGAS to explain four core metrics, building and running an evaluation set, and diagnosing issues so "it feels better" becomes "recall rose from 0.72 to 0.91."
A confidential VM or GPU label is not automatically trustworthy. This article uses RATS roles, EAT freshness, composite measurements, and policy-based key release to turn confidential inference into a verifiable control.
Testing OCR, ASR, or text answers in isolation misses cross-modal mismatches. This article uses asset alignment, missing/conflict slices, metamorphic relations, layered metrics, and shadow traffic to block inter-modal quality regressions.
The main variable cost in an LLM application is tokens, and it can quietly spiral out of control. This article explains five cost-reduction levers: prompt caching, model routing, prompt trimming, batch processing, and result caching, plus cost monitoring by feature and user to make AI products sustainable.
Two annotators assigning the same label does not necessarily mean the standard is clear, and disagreements cannot be resolved by letting the latest label overwrite earlier ones. This article combines item snapshots, blinded assignment, evidence spans, dispute adjudication, and label generations into a traceable gold-label contract.
The prompt-engineering era is yielding to context engineering: prompts care about 'how to say it,' context cares about 'what to show the model.' This article covers four context types, window-management strategies (priority/compression/forgetting), and how to build an Agent's context—its ceiling usually depends not on model strength but on what you feed it.
Dashboard screenshots and post-incident reruns cannot prove what happened at the time of an incident. This article freezes model, prompt, retrieval, and tool evidence; defines append-only sealing and transfer chains; and enables review while protecting sensitive source content.
GPU capacity cannot be derived directly from request counts. This guide builds a load-testable, reviewable inference capacity model using input and output token distributions, concurrent sequences, KV Cache, batching efficiency, and failure redundancy.
Clicking “Modify” does not automatically make an AI decision safe. This article binds the original machine recommendation, operator qualifications, override rationale, actual effect, and subsequent outcome into an immutable record while enforcing baselines that humans cannot bypass.