Context
Select, retrieve, compress, and isolate the information a model needs without treating a huge prompt as architecture.
FG / 03 / Field guide
Learn through useful work: bounded authority, visible failures, repeatable evaluations, and a real quality bar.
Current briefing
Get your bearings
Separate models, products, workflows, agents, and the harness that makes them dependable. A model demo is not a production system, and an agent is not merely a longer prompt. Reliability lives in the surrounding constraints and feedback loops.
Start here / Three moves
Define inputs, output artifact, quality bar, privacy class, unacceptable failures, time saved, and the human decision that remains.
Use explicit instructions, structured output, minimal tools, permission boundaries, logs, and a clear stop condition.
Save representative examples, known traps, latency, cost, tool failures, and review outcomes; rerun them after changes.
Build capability
Select, retrieve, compress, and isolate the information a model needs without treating a huge prompt as architecture.
Control tools, permissions, state, retries, handoffs, tracing, budgets, and durable work products.
Measure actual workflow quality with examples, graders, failure taxonomies, human review, and regression checks.
Choose gear / Decision rule
Choose models and platforms by the actual workflow: data sensitivity, quality, tool use, modalities, latency, cost, maintenance, auditability, retention, access control, and portability. Benchmark the work—not the launch demo.
Your source map
Also followed as a newsletter.
Idea source; verify independently.
Idea source; verify independently.
Idea source; verify independently.
Idea source; verify independently.
Idea source; verify independently.
Idea source; verify independently.
Reference layer
Original editorial work
Applied AI
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