All field notes

Prompts are production configuration and deserve the same discipline as code: versioning, review, testing, staged rollout, and rollback. This playbook covers building a prompt management system that provides it.

Some decisions cannot wait for a batch job: fraud, outages, safety events, and customer moments. This playbook covers building a real-time AI monitoring system from streaming ingestion to scoring, alerting, feedback, and evaluation.

A recommendation API has to be relevant, fast, and measurable in the same request. This playbook covers signals, candidate generation, ranking, low-latency serving, experimentation, and monitoring.

Salesforce holds the customer record and a rich permission model that an AI agent must inherit. This playbook covers permissions, data access, actions with approvals, integration with flows, governance, and evaluation.

Semantic search finds meaning, not just keywords, but only when it is combined with lexical search, reranking, and honest relevance evaluation. This playbook covers the build from indexing to operations.

Slack is where work already happens, which makes it the natural home for an AI assistant if it respects permissions, stays grounded, and acts only with approval. This playbook covers the build from app setup to evaluation.

Transcription quality decides what every downstream AI feature can do with voice. This playbook covers audio capture, model selection, diarization, custom vocabulary, post-processing, privacy controls, and evaluation.

A vector search service is shared infrastructure for RAG, semantic search, and recommendations. This playbook covers the embedding pipeline, index choice, filtering, multi-tenancy, latency, freshness, and evaluation.

RAG demos are easy; production LLM systems are not. How to outsource LLM and RAG development to a partner that handles retrieval, evaluation, and guardrails.

The ROI of a forward deployed engineer is the value of an outcome that finally ships versus the quiet cost of an initiative that stalls. How to build the case.

Generative AI talent is scarce and uneven. How to hire generative AI developers who ship production apps—and avoid paying for impressive demos that never scale.

Approval gates and exception queues are only real if humans can review fast with context. This playbook covers routing policies, reviewer interface, decision capture, staffing, feedback loops, and metrics.

Hybrid search combines exact-term precision with semantic recall and consistently beats either alone on enterprise queries. This playbook covers dual indexes, query processing, fusion, reranking, filters, and evaluation.

A knowledge base chatbot is the most common first LLM project and the most commonly under-built. This playbook covers curation, retrieval, grounded answers, refusal, evaluation, and launch so it earns trust.

Lead scoring works when sales trusts the score and acts on it. This playbook covers outcome definition, fit and intent features, calibrated modeling, CRM delivery, feedback loops, and evaluation on pipeline outcomes.

A model registry is the system of record for what is in production, how it was evaluated, and who approved it. This playbook covers metadata, versioning, lineage, evaluation records, approval, deployment tracking, and access.

Multi-agent systems fail from handoffs, loops, and untraceable behavior. This playbook covers when to decompose, typed contracts, deterministic orchestration, shared state, budgets, trajectory evaluation, and observability.

Some data cannot leave the perimeter, and some workloads justify owning the model. This playbook covers building a private LLM deployment: model selection, serving, security, gateway integration, evaluation, and operations.

Procurement is policy-heavy, approval-heavy, and full of requests that arrive as free text. This playbook covers intake, policy checks, supplier and catalog grounding, approvals, ERP integration, and evaluation.

When off-the-shelf AI does not fit, custom software wins. What custom AI development includes, when to build vs buy, and how to choose a partner that ships.

Documentation burden is where clinicians want AI most and where safety demands the most care. This playbook covers consent, capture, source-grounded drafting, clinician sign-off, safety validation, and evaluation.

A code review agent that floods pull requests with noise gets muted. One that finds real defects with evidence gets trusted. This playbook covers scope, context, analysis, noise control, evaluation, and rollout.

Contract analysis AI works when it reviews against a written playbook and shows lawyers the evidence. This playbook covers specification, extraction, deviation detection, review workflow, evaluation, and privilege controls.

Forecasts only matter when they change inventory and capacity decisions. This playbook covers data and hierarchy, probabilistic modeling, backtesting, planner integration, forecast value added, and monitoring.