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AI agents, RAG, and integrations — notes from production.

How to Write an AI Requirements Doc Engineers Can Build From

An AI requirements document needs the task with examples, acceptance criteria as an eval-set spec, the non-functional constraints, and failure behaviour.

AI Incident Response: What to Do When the Model Is Wrong at Scale

AI incident response: detect from observability, triage the layer, contain with a feature flag to rules or humans, then root-cause and add a test case.

The Total Cost of Owning a Custom AI System

Total cost of ownership for AI is build plus run plus the lifecycle costs that outlive the build: retraining, monitoring, on-call, eval upkeep, model migration.

How AI Vendors Price Their Products — and How to Compare Them

AI vendor pricing comparison: per-seat, per-resolution, per-token, per-request. Normalise every quote to cost per unit of work at your volume, including peak.

Guardrails for an LLM in Production: Input, Output, and Action

LLM guardrails in production come in three layers: input (injection, PII, off-topic), output (schema, unsupported claims), and action (what the tools can do).

When to Fine-Tune vs When to Prompt Better

Fine-tune vs prompt engineering: fine-tuning buys format consistency and lower per-call cost, not new knowledge. Exhaust prompting, few-shot, and RAG first.

LLM Observability: What to Log and Alert On

LLM observability: trace each request end to end - prompt, retrieval, tool calls, output, tokens, latency - and alert on refusals, tool errors, cost, and drift.

Data Readiness: What Your Data Must Look Like Before an AI Project

Data readiness for an AI project: an API to reach the data, labelled outcomes, current content, and enough history - assess it before committing a timeline.

Measuring Whether an AI Feature Actually Moved the Business Metric

AI feature ROI measurement: use a holdout, define the metric and owner before launch, and net out deflection that isn't resolution, seasonality, re-contacts.

How to Choose Between an AI Agent and a Fixed Workflow

AI agent vs workflow: an agent decides the steps at runtime; a workflow runs steps you coded with the LLM in slots. Prefer the workflow when the path is known.